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 .
This action is responsive to communication filed on 11/14/2025.
Claims 1-23 are pending. Claims 21-23 are new. Claims 14, 17 and 20 have been cancelled. Claims 15-16 and 18-19 have been withdrawn from consideration as been directed to a non-elected invention. Claims 15-16 and 18 have been amended. Entry of this amendment is accepted and made of record.
Election/Restrictions
Applicant’s election without traverse of Invention I, (claims 1-13) in the reply filed on 11/14/2025 is acknowledged.
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 1-13 and 21-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 13 recites the limitation "based on first sample measurement data relating to the one or more first operating parameters" in lines 5-6 and 10-11 of claim 1 and in lines 9-10 and 14-15 of claim 13 respectively. There is insufficient antecedent basis for this limitation in the claim.
Regarding claims 1 and 13 the recitation "based on first sample measurement data relating to the one or more first operating parameters" in line 5-6 and 10-11 of claim 1 and in lines 9-10 and 14-45 of claim 13 respectively renders the claim indefinite. It is unclear from the claim whether the claimed first sample measurement data relating to the one or more first operating parameters is the same or different to the claimed “first measurement data relating to one or more first operating parameters of the target device. Clarification and correction is required.
Claim 9, recites the limitation “second sample measurement data of the reference device” recited in line 4 of the claim renders the claim indefinite. There is insufficient antecedent basis for this limitation on the claim. Clarification and correction is required.
Regarding claim 9, the recitation “second sample measurement data of the reference device” recited in line 4 and the recitation of “the second sample measurement data” in lines 7 of the claim renders the claim indefinite. It is unclear from the claim whether the recited “second sample measurement data” is the same or different from the second measurement data recited by its preceding claims 1 and 7 from which it depends. Clarification and correction is required.
Claim 9, recites the limitation “second sample measurement data of the reference device” recited in line 4 of the claim renders the claim indefinite. There is insufficient antecedent basis for this limitation on the claim. Clarification and correction is required.
Dependent claims 2-12 and 21-23 are rejected under 35 USC 112(b) for the reasons discussed above with respect to their respective independent claims 1 and 13.
For examination on the merits the claims are interpreted as best understood in light of 35 USC 112(b) rejections above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Under Step 1 of the analysis, claim 1, belongs to a statutory category namely a method. Likely claim 13 , belongs to a statutory category, namely it is a system.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim(s) 1, and 13 recite(s) concepts related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion for “obtaining a correlation model corresponding to the target device, the correlation model being generated based on first sample measurement data relating to the one or more first operating parameters and one or more second operating parameters of a reference device, the reference device being of a same type of device as the target device and equipped with one or more additional sensors compared with the target device, the one or more additional sensors being configured for collecting the first sample measurement data relating to the one or more second operating parameters; and predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device”.
The concepts discussed above can be considered to describe mental processes, namely concepts performed in the human mind or with pen and paper, and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers. Although, the claim does not spell out any particular equation or formula being used, the lack of specific equations for individual steps merely points out that the claim would monopolize all possible calculations in performing the steps. These steps recited by the claims, therefore amount to a series of mental or mathematical steps, making these limitations amount to an abstract idea.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
This judicial exception is not integrated into a practical application because the abstract idea is not performed by using any particular device and because the “system for device monitoring comprising:… at least one processor configured to communicate with the at least one storage device” recited by claims 13, amounts to the recitation of a general purpose computer used to apply the abstract idea; the recitation of “obtaining first measurement data relating to one or more first operating parameters of the target device ” recited by claims 1 and 13, is mere gathering recited at high level of generality and the results of the algorithm are merely output/stored as part of insignificant post-solution activity and are not used in any particular matter as to integrate the abstract idea in a practical application.
Under Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer “system for device monitoring comprising…at least one processor…” (claim 13), used to apply the abstract idea and mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself.
Therefore, claims 1, and 13 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-12 and 21-23 merely expand on the abstract idea by appending additional steps to the mathematical algorithm on their respective independent claims 1 and 13.
Dependent claims 2-12 and 21-23 merely expands on the abstract idea by reciting additional steps related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion and mere characterization of the data acquired and applied for performing the abstract idea i.e. modifying, based on the environment data and a first environment model, the second measurement data (claims 2 and 21), “modifying, based on the environment data and a first environment model, the second measurement data comprises: selecting, from the plurality of association rules, a target association rule corresponding to an environment of the target device; modifying, based on the first measurement data and the target association rule, the second measurement data” (claims 3 and 22), “selecting, from a plurality of candidate correlation models each of which corresponds to one of a plurality of types of environment, the correlation model based on the environment data” (claims 4 and 23), “wherein the predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device comprises: … predicting the second measurement data based on the first measurement data, the correlation model, and the environment data, the correlation model being generated based on sample environment data of the reference device” (claim 5), “assessing an operating state of the target device based on the first measurement data and the second measurement data” (claim 6), “assessing an operating state of the target device based on the first measurement data and the second measurement data further comprising: determining, based on the first measurement data and the second measurement data, an assessment score of the operating state of the target device using a performance evaluation model” (claim 7), “modifying, based on environment data, the assessment score using a second environment model” (claim 8), “sample assessment score being determined based on sample image data collected by the reference device under the second sample measurement data; and generating the performance evaluation model by training a preliminary performance evaluation model using the plurality of training samples” (claim 9), “he performance evaluation model includes a performance degradation evaluation model” (claim 10), “obtaining an operating parameter determination model corresponding to the target device; and determining one or more parameter values of the one or more first operating parameters and the one or more second operating parameters to be used by the target device at a future time based on the first measurement data, the second measurement data, and the operating parameter determination model” (claim 11), “wherein the one or more parameter values of the one or more first operating parameters and the one or more second operating parameters to be used by the target device are determined further based on the environment data” (claim 12) and mere characterization of the data acquired and applied for performing the abstract idea i.e. “herein the first environment model includes a plurality of association rules between the one or more first operating parameters and the one or more second operating parameters, each of the plurality of association rules corresponding to one of a plurality of types of environment” (claims 3 and 22), “each of the plurality of training samples including second sample measurement data of the reference device and a sample assessment score of an operating state of the reference device” (claim 9).
This judicial exception is not integrated into a practical application in claims 2-12 and 21-23 because the abstract idea is not performed by using any particular device and because the “system” recited in claims 21-23, amounts to the recitation of a general purpose computer used to apply the abstract idea; and because the recitation of “obtaining environment data of the target device” recited in claims 2 and 21, “obtaining environment data of the target device” recited in claims 4, 5 and 23, “obtaining a plurality of training samples, each of the plurality of training samples including second sample measurement data of the reference device and a sample assessment score of an operating state of the reference device” (claim 9), “obtaining environment data of the target device” (claim 12) amounts to mere data gathering recited at a high level of generality, the limitations merely add further details as to the type of data, the means of collecting data being received/input/stored (memory) and used with the mental process and/or math steps recited in the independent claims, also further calculations and math, so they are properly viewed as part of the recited abstract idea; and the results are not used in any particular matter as to integrate the abstract idea in a practical application.
The claim(s) claims 2-12 and 21-23 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are general purpose computer used to apply the abstract idea and mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself.
Therefore claims 1-13 and 21-23 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-13 and 21-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Page et al. US2020/0013501A1 (hereinafter Paige).
Regarding claim 1, Page disclose a method for device monitoring (abstract), comprising:
obtaining first measurement data relating to one or more first operating parameters of a target device (see abstract, para. 0001-0003, 0005, 0014, 0025, 0031);
obtaining a correlation model corresponding to the target device, the correlation model being generated based on first sample measurement data relating to the one or more first operating parameters and one or more second operating parameters of a reference device, the reference device being of a same type of device as the target device and equipped with one or more additional sensors compared with the target device, the one or more additional sensors being configured for collecting the first sample measurement data relating to the one or more second operating parameters (para. 0080, 0089, wherein comparing models (correlation model) are disclosed and wherein component performance may be determined by a comparison to other MDD of historical (reference device) operations of the same or other monitored (same type) devices and wherein degradation in MDD values over time indicating that related component is wearing out may be identified by component performance, para. 0025, 0070, 0080, 0084, 0095, wherein sensors are disclosed; Fig. 4, para. 0066); and
predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device (para 0046, 0048, 0060, 0073, 0080, 0082, 0084, wherein prediction is disclosed).
Regarding claim 2, Page further disclose the method further comprises: obtaining environment data of the target device (see para. 0025, 0066, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed); and
modifying, based on the environment data and a first environment model, the second measurement data (see para. 0027, wherein data may be normalized; para. 0039, wherein medical device data can be provided to provide monitoring, reporting and/or control applications based on the analyzed streams of medical device data; 0080, 0082, 0083, 0084, 0085, wherein calibration may be made and wherein were updates of functions, scheduling and maintenance inspection and management is disclosed; para. 0089-0090, wherein test settings and corrections is disclosed; 0097, wherein changes in gas flow rates, in system pressure or other medical device operations is disclosed).
Regarding claim 3, Page further disclose wherein the first environment model includes a plurality of association rules between the one or more first operating parameters and the one or more second operating parameters, each of the plurality of association rules corresponding to one of a plurality of types of environment (see para. 0089, wherein a plurality of detection rules is disclosed corresponding/associated with operating parameters i.e. gas pressures, flow rates, or operation of particular components), and the modifying, based on the environment data and a first environment model, the second measurement data comprises: selecting, from the plurality of association rules, a target association rule corresponding to an environment of the target device (see para. 0025, 0089-0090, wherein detection rules are discussed and particular test settings or routines are identified i.e. gas pressures, flow rates or operation of particular components and wherein other procedural events are disclosed; para. 0094, wherein analytics rules are applied to the time series MDD); modifying, based on the first measurement data and the target association rule, the second measurement data (see para. 0089, wherein a plurality of rules is disclosed; para. 0090, wherein additional analysis of keystroke data is performed and additional analysis of the data provide information regarding interactions between clinician an machine prior to alarm, event or notification to be corrected in order to improve determination of maintenance recommendations provided by the system, and wherein test settings and corrections is disclosed; 0097, wherein changes in gas flow rates, in system pressure or other medical device operations is disclosed).
Regarding claim 4, Page further disclose, the obtaining a correlation model corresponding to the target device further comprising: obtaining environment data of the target device (see para. 0025, 0051, 0066, 0068, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed); and selecting, from a plurality of candidate correlation models each of which corresponds to one of a plurality of types of environment, the correlation model based on the environment data (see para. 0025, 0051, 0066, 0068, 0070, 0089, wherein direct indication that a checkout procedure have been performed or the MDD processing system can apply detection rules to identify if the checkout procedure has been performed and wherein identification of particular test settings of routines i.e. gas pressures, flow rates, or operation of particular components is disclosed).
Regarding claim 5, Page further disclose wherein the predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device (para 0046, 0048, 0060, 0073, 0080, 0082, 0084) comprises: obtaining environment data of the target device (see para. see para. 0025, 0051, 0066, 0068, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed); and predicting the second measurement data based on the first measurement data, the correlation model, and the environment data, the correlation model being generated based on sample environment data of the reference device (see 0001, 0045-0046, 0048, 0060, wherein predictive scheduling is based upon collected and processing medical device data, predictive algorithms are applied; see para. 0073, where detected phases of active procedure can be used to predict when active procedure will be completed and maintenance can be performed between procedures; para 0080, 0082, 0084, 0089, wherein predicted maintenance tasks are identified and based on predicted maintenance tasks information is provided and maintenance instructions are produced and wherein direct indication that a checkout procedure have been performed or the MDD processing system can apply detection rules to identify if the checkout procedure has been performed and wherein identification of particular test settings of routines i.e. gas pressures, flow rates, or operation of particular components is disclosed).
Regarding claim 6, Page disclose assessing an operating state of the target device based on the first measurement data and the second measurement data (see abstract, wherein medical device data is analyzed to determined operational status of a component of a medical device; para. 0004-0005, 0014, wherein machine data can include settings and measured data; 0063, wherein operation al status can be determined from the streams of machine data; 0073, 0075, 0079-0080).
Regarding claim 7, Page disclose the assessing an operating state of the target device based on the first measurement data and the second measurement data further comprising: determining, based on the first measurement data and the second measurement data, an assessment score of the operating state of the target device using a performance evaluation model (para. 0063, wherein data can be analyzed to detect performance of a checkout procedure by recognizing keystroke sequences, wherein MDD processing may identify the occurrence of the checkout procedure from the MDD, the test may produce an expected, output and output duration if medical equipment properly completes checkout procedure, wherein operational values and/or timing duration are disclosed; para. 0067, wherein performance may be measured and reported, wherein it may be represented as a fleet utilization rate or the percentage of time that each device is used during each day, and may be a composite metric that weights some or all of the metrics disclosed; para. 0080, wherein performance determination is performed by MDD processing system and based upon comparing models representative of particular component breakdown or wear to the streaming time series of MDD and may be determined by a comparison to other MDD of historical operations).
Regarding claim 8, Page further disclose modifying, based on environment data, the assessment score using a second environment model (see para. 0025, 0066, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed; para. 0063, wherein performance determination is made upon using measured data, wherein operational values and/or timing duration are disclosed; para. 0080, wherein performance may be determined by comparison of other MDD of historical operations of same or other monitored devices and wherein degradation in MDD values over time may be identified, para. 0084, wherein component performance is used and wherein calibration may be made based upon actual time of use or time or fuse phase duration).
Regarding claim 9, Page further disclose wherein the performance evaluation model is obtained according to a process including: obtaining a plurality of training samples, each of the plurality of training samples including second sample measurement data of the reference device and a sample assessment score of an operating state of the reference device (para. 0080, performance may be determined by comparison to other MDD of historical operations of same or other monitoring devices and current MDD may be compared to a model based upon historical MDD which reflect a normal or properly functioning component), the sample assessment score being determined based on sample image data collected by the reference device under the second sample measurement data (see para. 0080,0085 wherein performance may identify degradation in MDD values over time and where a model based upon historical data is disclosed; para. 0022, 0028, 0032, wherein imaging devices i.e. X-ray, CT, MRI, and ultrasound devices may be examples of medical devices and may include video and/or audio recording devices, and wherein image data and/or vide data is disclosed); and generating the performance evaluation model by training a preliminary performance evaluation model using the plurality of training samples (para. 0015-0016, 0057, 0100, wherein deep learning training is disclosed; 0080-0081, 0101-0102, wherein performance monitoring may be created over time through the use of deep learning analysis of the machine data and in analysis of machine operational status).
Regarding claim 10, Page further disclose wherein the performance evaluation model includes a performance degradation evaluation model (see para. 0080, wherein component performance may identify a degradation in the MDD values; para. 0083, wherein degraded component performance is disclosed; para. 0095 wherein performance degradation is identified).
Regarding claim 11, Page further disclose obtaining an operating parameter determination model corresponding to the target device (para. 0080,0082-0083 ); and determining one or more parameter values of the one or more first operating parameters and the one or more second operating parameters to be used by the target device at a future time based on the first measurement data, the second measurement data, and the operating parameter determination model (see para. 0044, 0046, wherein prediction of medical device needing maintenance is disclosed, 0053, 0073, 0079-0080, 0082, 0095, 0104 wherein time series MDD data is used for monitoring where data such as gas flows or particular operations or settings and whether components performance by a comparison to other MD of historical operations of the same or other monitored devices and performance of a components or system to identify degradation in MDD values over time is disclosed; para. 0082, wherein predictive maintenance tasks are identified and operations can be identified based on the multiple possible maintenance tasks).
Regarding claim 12, Page further teach obtaining environment data of the target device (see para. 0025, 0066, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed), wherein the one or more parameter values of the one or more first operating parameters and the one or more second operating parameters to be used by the target device are determined further based on the environment data (see para. 0080, 0089, wherein operating parameters of medical device is based on the environment data measured and a diagnosis is performed based on measured data and historical operation of same or other monitored devices).
Regarding claim 13, Page disclose a system for device monitoring, comprising:
at least one storage device including a set of instructions (para. 0018, 0019, 0026); and
at least one processor (para. 0019-0020 “processors” “processing system”) configured to communicate with the at least one storage device, wherein, when the instructions are executed, the at least one processor is configured to instruct the system to perform operations (see para. 0026), including:
obtaining first measurement data relating to one or more first operating parameters of the target device (see abstract, para. 0001-0003, 0005, 0014, 0025, 0031);
obtaining a correlation model corresponding to the target device, the correlation model being generated based on first sample measurement data relating to the one or more first operating parameters and one or more second operating parameters of a reference device, the reference device being of a same type of device as the target device and equipped with one or more additional sensors compared with the target device, the one or more additional sensors being configured for collecting the first sample measurement data relating to the one or more second operating parameters (para. 0080, 0089, wherein comparing models (correlation model) are disclosed and wherein component performance may be determined by a comparison to other MDD of historical (reference device) operations of the same or other monitored (same type) devices and wherein degradation in MDD values over time indicating that related component is wearing out may be identified by component performance, para. 0025, 0070, 0080, 0084, 0095, wherein sensors are disclosed; Fig. 4, para. 0066); and
predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device (para 0046, 0048, 0060, 0073, 0080, 0082, 0084, wherein prediction is disclosed.
Regarding claim 21, Page further disclose the method further comprises: obtaining environment data of the target device (see para. 0025, 0066, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed); and
modifying, based on the environment data and a first environment model, the second measurement data (see para. 0027, wherein data may be normalized; para. 0039, wherein medical device data can be provided to provide monitoring, reporting and/or control applications based on the analyzed streams of medical device data; 0080, 0082, 0083, 0084, 0085, wherein calibration may be made and wherein were updates of functions, scheduling and maintenance inspection and management is disclosed; para. 0089-0090, wherein test settings and corrections is disclosed; 0097, wherein changes in gas flow rates, in system pressure or other medical device operations is disclosed).
Regarding claim 22, Page further disclose wherein the first environment model includes a plurality of association rules between the one or more first operating parameters and the one or more second operating parameters, each of the plurality of association rules corresponding to one of a plurality of types of environment (see para. 0089, wherein a plurality of detection rules is disclosed corresponding/associated with operating parameters i.e. gas pressures, flow rates, or operation of particular components), and the modifying, based on the environment data and a first environment model, the second measurement data comprises: selecting, from the plurality of association rules, a target association rule corresponding to an environment of the target device (see para. see para. 0025, 0066, 0070, 0089, 0089-0090, wherein detection rules are discussed and particular test settings or routines are identified i.e. gas pressures, flow rates or operation of particular components and wherein other procedural events are disclosed; para. 0094, wherein analytics rules are applied to the time series MDD); modifying, based on the first measurement data and the target association rule, the second measurement data (see para. 0089, wherein a plurality of rules is disclosed; para. 0090, wherein additional analysis of keystroke data is performed and additional analysis of the data provide information regarding interactions between clinician an machine prior to alarm, event or notification to be corrected in order to improve determination of maintenance recommendations provided by the system, and wherein test settings and corrections is disclosed; 0097, wherein changes in gas flow rates, in system pressure or other medical device operations is disclosed).
Regarding claim 23, Page further disclose, the obtaining a correlation model corresponding to the target device further comprising: obtaining environment data of the target device (see para. 0025, 0051, 0066, 0068, 0070, 0089, wherein flow rates, pressure, gas concentration is disclosed); and selecting, from a plurality of candidate correlation models each of which corresponds to one of a plurality of types of environment, the correlation model based on the environment data (see para. 0025, 0051, 0066, 0068, 0070, 0089, wherein direct indication that a checkout procedure have been performed or the MDD processing system can apply detection rules to identify if the checkout procedure has been performed and wherein identification of particular test settings of routines i.e. gas pressures, flow rates, or operation of particular components is disclosed).
Conclusion
The prior art made of record cited in form PTOL-892 and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. US 20170309061 A1 disclose a method for image rendering. The method may include obtaining first image data by a first processing device; performing a first rendering operation on the first image data; and determining second image data based on the first image data. The method may further include receiving a non-rendering operation on the second image data by the second processing device; and performing a second rendering operation on the non-rendered second image data (see abstract).
Sehgal et al. US 20140266713 A1 disclose a methods for generating an alert to perform maintenance on a medical device based on actual usage of medical device. Measuring a parameter value for a parameter type associated with a component in a medical device connected to a network and related to a function performed by the component. The method further comprises assessing a maintenance threshold value that specifies a performance limit for the parameter type, comparing the maintenance threshold value with the measured parameter value and generating an alert based on the comparing to indicate the maintenance is needed for the component (see abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YARITZA H PEREZ BERMUDEZ whose telephone number is (571)270-1520. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857