DETAILED ACTION
Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
3. 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.
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Varia (US 2022/0391916).
In claim 1, Varia teaches
A system comprising:
a plurality of servers configured to store a plurality of equipment lifecycle datasets ([0036] the utility equipment may include a heavy machine vehicle like trucks, tree-shaker, railway maintenance or construction and material handling portable machines, a consumer electronic product, a portable or stationary industrial machine such as compressors, chillers, and the like [0038] the data obtaining module 212 collects the usage data of the one or more authenticated components, the one or more authenticated services or a combination thereof via the one or more communication platforms 110 for transmission of the usage data to the cloud-based platform in real time. The one or more communication platforms 110 collect the usage data of the one or more authenticated components, the one or more authenticated services or a combination thereof by the one or more users and transmits to the cloud-based platform. The cloud-based platform also includes a usage data storage repository which stores the usage data collected via the one or more communication platforms 110);
a display ([0090] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers [0092] a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example); and
a visualization server in operable communication with the plurality of servers and the display, wherein the visualization server comprises a processor and a non-transitory memory storing instructions, that, when executed, cause the processor to ([0049] the customer may visualize the dynamic score and the weight profile of the model of the utility equipment from a digital platform. In such embodiment, the digital platform may include a website published on a web server to depict information about the model of the utility equipment, the asset score, various weight profiles and the like):
receive the plurality of equipment lifecycle datasets from the plurality of servers ([0038] the usage data may include data of usage of the one or more authenticated components in a predefined time period, data of usage of the one or more authenticated services in the predefined time period, data of usage of the utility equipment in a predefined geographical location, data of installation event, data of maintenance service performed, data of usage of the utility equipment based on guidance provided in a handbook or a combination thereof. In an embodiment of the present disclosure, the usage data also includes location i.e., latitude and longitude of the one or more authenticated components, the one or more authenticated services or a combination thereof, time of the event, installation type, and the like which are automatically captured. In an embodiment of the present disclosure, a firmware in the gateway captures these parameters and send it to the storage unit 206. In an exemplary embodiment of the present disclosure, the installation type may include factory install, replacement, repair, regular maintenance, and the like. Further, the usage data is sent along with the unique encrypted code and timestamp as payload to the storage unit 206. In an embodiment of the present disclosure, once the unique encrypted code is transmitted to the storage unit 206 along with the timestamp in the payload, the timestamp is used to know when a component was installed and when it is getting expired based on the lifetime of that component. For example, an air filter is installed on time t1, air filter needs to be replaced every 3 months, an alert may be triggered after 3 months that air filter replacement is due. Further, when the air filter is installed, it may be detected whether its unique and genuine [0042] the asset score signifies the operational status of the utility equipment and a score between [0-100] represents how good or bad the utility equipment is maintained over its lifetime);
automatically extract, from the plurality of equipment lifecycle datasets, a visualization dataset ([0048] The data output module 222 is configured to output the generated one or more notifications, the predicted rate of variation and the determined health condition on user interface screen of the one or more electronic devices 108 associated with the one or more users via the one or more communication channels. In an exemplary embodiment of the present disclosure, the one or more communication channels include a Short Message Service (SMS), a multimedia message, a push notification on mobile phone, an email, and the like. In an embodiment of the present disclosure, the predicted rate of variation is outputted in one or more visualization formats. The one or more visualization formats include charts, graphs, or a combination thereof. The one or more visualization formats include charts, graphs, or a combination thereof); and
output the visualization dataset for display by the display ([0057] predicted result generated by the data prediction module 216 is depicted in one or more visualization formats 314 such as charts, graphs and the like for the customer).
In claim 2, Varia teaches
The system of claim 1, wherein the non-transitory memory further comprises a trained machine learning model ([0043] CNN-QR is a proprietary machine learning technique for forecasting time series using causal CNNs. CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata and is the only forecast technique that accepts related time series data without future values. Further, the DeepAR+ is a proprietary machine learning technique for forecasting time series using recurrent neural networks (RNNs)).
In claim 3, Varia teaches
The system of claim 2, wherein the equipment lifecycle datasets comprise purchasing data, deployment data, and repair data for a set of equipment under analysis ([0039] the data obtaining module 212 obtains the one or more events and the timing of the one or more events associated with the utility equipment from the storage unit 206 based on the obtained usage data and the obtained one or more utility parameters. In an exemplary embodiment of the present disclosure, the one or more events include one or more periodic scheduled maintenance events, one or more repair events, one or more accident events, one or more audit events, one or more financial events, one or more routing maintenance events or any combination thereof [0049] the data computation module 214 generates a dynamic resale value based on the generated one or more dynamic parameters, the asset score, a prior purchase price of the utility equipment and a quality of maintenance of the utility equipment).
In claim 4, Varia teaches
The system of claim 2, wherein the machine learning model is a time-series forecasting model ([0043] The data prediction module 216 is configured to predict the rate of variation of the asset score associated with the timings of the one or more events of the utility equipment based on the plurality of historical asset scores, the one or more new events, the generated weight profile, and the generated asset score by using the variation prediction-based AI model. In an exemplary embodiment of the present disclosure, the rate of variation includes an incremental rate, a decremental rate of the asset score or a combination thereof. In an embodiment of the present disclosure, the variation prediction-based AI model is a forecasting model that predicts the future values based on past performance. . For example, … Non-Parametric Time Series (NPTS). In an embodiment of the present disclosure, CNN-QR is a proprietary machine learning technique for forecasting time series using causal CNNs. NPTS is especially useful when working with sparse or intermittent time series. Forecast provides four algorithm variants: standard NPTS, seasonal NPTS, climatological forecaster, and seasonal climatological. Further, ARIMA is a commonly used statistical technique for time-series forecasting. ARIMA is especially useful for simple datasets with under 100 time series. Furthermore, ETS is a commonly used statistical technique for time-series forecasting).
In claim 5, Varia teaches
The system of claim 4, wherein the visualization dataset comprises an output of a trained machine learning model ([0057] predicted result generated by the data prediction module 216 is depicted in one or more visualization formats 314 such as charts, graphs and the like for the customer).
In claim 6, Varia teaches
The system of claim 1, wherein the visualization dataset comprises an estimate of lifecycle utilization ([0049] the data computation module 214 generates a dynamic resale value based on the generated one or more dynamic parameters, the asset score, a prior purchase price of the utility equipment and a quality of maintenance of the utility equipment. In an embodiment of the present disclosure, the dynamic resale value may be better if the one or more genuine parts are used at a right time, which in turn maintains the quality. Further, the asset score may be less if the one or more parts used in the utility equipment are not genuine which in turn degrades the quality of maintenance of the utility equipment. Furthermore, the dynamic resale value of the utility equipment may be determined for a second owner and a third owner based on the prior purchase price of the utility equipment).
In claim 7, Varia teaches
The system of claim 1, wherein the visualization dataset comprises an estimate of lifecycle cost of ownership ([0049] The data computation module 214 determines an age of the utility equipment based on the generated one or more dynamic parameters, the asset score, and the quality of maintenance of the utility equipment. In an embodiment of the present disclosure, the dynamic score provides data in case of legacy equipment. Based on the determined age and algorithmic data, a certified technician having the right skills may be dispatched to replace one or more defected parts with the one or more authorized parts. Further, the asset score is also used to determine the technician's rate dynamically and cost of the overall replacement with guarantee).
Claims 8-14 are essentially same as claims 1-7 except that they recite claimed invention as a computer-implemented method and are rejected for the same reasons as applied hereinabove.
Claims 15-20 are essentially same as claims 1-7 except that they recite claimed invention as a non-transitory computer readable medium and are rejected for the same reasons as applied hereinabove.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed on 892 form.
Examiner’s Note: Examiner has cited particular figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUAWEN A PENG whose telephone number is (571)270-5215. The examiner can normally be reached Mon thru Fri 9 am to 5 pm.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUAWEN A PENG/Primary Examiner, Art Unit 2169
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