DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
2. This communication is in response to the communication filed 6/12/2024. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 103
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3.1. Claims 1-5, 8-9, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Jain (US 2011/0166628), in view of Cella et al. (US 2019/0339688).
CLAIM 1
Jain teaches a computer-implemented method (Jain: abstract) comprising:
receiving, by one or more processors of a computer system, one or more data packets from a controller of a [device] over a network, the one or more data packets including data associated with technical parameters of the [device] at a given state of the [device] (Jain: abstract; ¶¶ [0035] “application hosting device 14 and the medical device 12 may include one or multiple processors and memory. In some embodiments, the processor may be a microprocessor, a controller, or a microcontroller”, [0038] “application hosting device 14 may also include a WAN packets exchange module 24 configured to process data packets that are exchanged through a WAN network… packet parsers module 32 is configured to parse data packets, such as the data packets received from the medical devices”, [0055] “packet 54 may therefore essentially embed…other information (e.g., make, model, serial #, battery level, status, configuration, calibration, password, security key, encryption mode, etc.) about medical device”; FIGS. 1-16);
converting, by the one or more processors, the data contained in the data packets to a digital report of the given state of the compressor in a human readable format (Jain: abstract; ¶¶ [0060] “Data may be stored in a plain text file for easy viewing on the application hosting device”, [0061] “Using the medical device data, the system information of the application hosting device 14, and information of the medical device 12, a data packet 54 is created. In one embodiment, the data packet 54 is created in XML form”—XML format is considered a human readable format because it can be easily understood and interpreted by people without specialized software, [0062]; FIGS. 1-16); and
storing, by the one or more processors, the digital report in one or more databases accessible through an online portal associated with the computer system (Jain: abstract; ¶¶ [0035] “user interface enables a user, such as a patient, doctor, clinician, or other health care provider, or anyone using the application hosting device 14, to interact with the application hosting device 14 such that the user can, for example, input requests for information from or input instructions to be submitted to, the medical device”, [0036] “application hosting device 14 and/or data processing server 16 may be connected (e.g., via the Internet or other communication network or mechanism) to one or more third party applications or systems”, [0060] “extracted information is then stored in a data store”, [0055] “web application 56 may be hosted on the application hosting device” and “web services API 58 may be operatively connected to a database 55 to store and retrieve the information”; FIGS. 1-16).
Jain does not appear to explicitly teach the following:
a compressor.
Cella, however, teaches the following:
a compressor (Cella: abstract; ¶¶ [0391] “platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for…medical diagnostics, health monitoring, and the like, [0665]-[0666] “compressors”, [0825], [4376] “medical equipment”; FIGS. 1-83).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the method and system for monitoring and maintenance of equipment such as compressors, as taught by Cella, with the system and method and device for medical device processing and management, as taught by Jain, with the motivation of facilitating the maintenance of devices (Cella: ¶¶ [0003]-[0010]).
CLAIM 2
Jain does not appear to explicitly teach the computer-implemented method of claim 1, further comprising: using, by the one or more processors, a trained artificial intelligence model to extract, from the one or more data packets, a predicted failure event of the compressor and a corrective action, by inputting the data from the one or more data packets transmitted by the controller of the compressor.
Cella, however, teaches further comprising: using, by the one or more processors, a trained artificial intelligence model to extract, from the one or more data packets, a predicted failure event of the compressor and a corrective action, by inputting the data from the one or more data packets transmitted by the controller of the compressor (Cella: abstract; ¶¶ [0042] “corrective actions may be identified and taken in response to the state-related measurements captured”, [0290] “Machine learning may use one or more models” and “machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like”, [0985], [2738] “identify one or more issues (e.g., faults or potential failures of one or more components), determine a corrective action (e.g., alter an operating speed of a device associated with the faulty or failing component), and initiate the corrective action (e.g., automatically analyze data, identify issues, determine corrective action, and carry out, at least part of, the corrective action)”, [2789] “embodiments of the present disclosure, including those involving expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes. References to a neural net throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like”; FIGS. 109-136).
The motivation to include the teachings of Cella with the teachings of Jain is the same as that of claim 1 above and is incorporated herein.
CLAIM 3
Jain does not appear to explicitly teach the computer-implemented method of claim 1, further comprising: training, by the one or more processors, the trained artificial intelligence model by feeding input datasets of actual failure events of the compressor and known solutions to avoid or correct the actual failure event into an artificial intelligence model to output a predicted failure event of the compressor and a corrective action to avoid the predicted failure event; building, by the one or more processors, a knowledge graph using a relative relationship of at least two nodes of the input datasets of actual failure events; and translating, by the one or more processors, the knowledge graph into machine readable natural language.
Cella, however, teaches , further comprising: training, by the one or more processors, the trained artificial intelligence model by feeding input datasets of actual failure events of the compressor and known solutions to avoid or correct the actual failure event into an artificial intelligence model to output a predicted failure event of the compressor and a corrective action to avoid the predicted failure event; building, by the one or more processors, a knowledge graph using a relative relationship of at least two nodes of the input datasets of actual failure events; and translating, by the one or more processors, the knowledge graph into machine readable natural language (Cella: abstract; ¶¶ [0042] “corrective actions may be identified and taken in response to the state-related measurements captured”, [0290] “Machine learning may use one or more models” and “machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like”, [0398] “machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes”, [0397] “graphical models”, [0985], [2738] “identify one or more issues (e.g., faults or potential failures of one or more components), determine a corrective action (e.g., alter an operating speed of a device associated with the faulty or failing component), and initiate the corrective action (e.g., automatically analyze data, identify issues, determine corrective action, and carry out, at least part of, the corrective action)”, [2789] “embodiments of the present disclosure, including those involving expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes. References to a neural net throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like”, [4202]; “CMMS subsystem 28622 may interface with one or more predictive maintenance knowledge bases and/or knowledge graphs that may be stored in a data store accessible by the CMMS subsystem. In embodiments, such a CMMS knowledge base or the like may further include a knowledge graph that may contain information beneficial to the service determination and order generation services” FIGS. 109-136).
The motivation to include the teachings of Cella with the teachings of Jain is the same as that of claim 1 above and is incorporated herein.
CLAIM 4
Jain teaches the computer-implemented method of claim 1, wherein the controller includes a graphical user interface that, only in response to a physical input, allows a transmission of the one or more data packets over the network to be received by the computer system (Jain: abstract; ¶¶ [0035] “provides a graphical user interface…user interface may include a display and an input device, which may be a touch screen, a mouse pointer, a keyboard, or other device. The input device may be integral with the display or may be separate and configured to interact with the display. The user interface enables a user, such as a patient, doctor, clinician, or other health care provider, or anyone using the application hosting device 14, to interact with the application hosting device 14 such that the user can, for example, input requests for information from or input instructions to be submitted to, the medical device”; FIGS. 1-16).
CLAIM 5
Jain does not appear to explicitly teach the computer-implemented method of claim 4, further comprising: augmenting, by the one or more processors, the graphical user interface of the controller to display a new icon indicative of a potential failure event, in response to a trained artificial intelligence model outputting a predicted failure event based on the one or more datasets being fed into the trained artificial intelligence model by the one or more processors.
Cella, however, teaches further comprising: augmenting, by the one or more processors, the graphical user interface of the controller to display a new icon indicative of a potential failure event, in response to a trained artificial intelligence model outputting a predicted failure event based on the one or more datasets being fed into the trained artificial intelligence model by the one or more processors (Cella: abstract; ¶¶ [0429] “heat map interface may alert a user by showing a machine in bright red. If a system is experiencing unusual vibrations, the heat map interface may show a different color for a visual element for the machine, or it may cause an icon or display element representing the machine to vibrate in the interface, calling attention to the element”, [0431] “signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR/VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations. In many examples, colors, shapes, and sizes of visual overlay elements may represent varying levels of input along the relevant dimensions for a sensor or combination of sensors. In further examples, if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display”; FIGS. 1-83).
The motivation to include the teachings of Cella with the teachings of Jain is the same as that of claim 1 above and is incorporated herein.
CLAIM 8
Jain teaches the computer-implemented method of claim 1, wherein the converting the data from the one or more data packets to the digital report comprises formatting from one data file type to another data file type (Jain: abstract; ¶¶ [0060] “Data may be stored in a plain text file for easy viewing on the application hosting device”, [0061] “Using the medical device data, the system information of the application hosting device 14, and information of the medical device 12, a data packet 54 is created. In one embodiment, the data packet 54 is created in XML form, [0062]; FIGS. 1-16).
CLAIM 9
Jain does not appear to explicitly teach the computer-implemented method of claim 1, wherein the technical parameters of the compressor include a system pressure, an ambient temperature, a compressor discharge temperature a line pressure, a speed of a variable speed drive, a dew point, a carbon monoxide level, a carbon dioxide level, an operating mode of a component of the compressor, a running time of the component, average daily runtime of the compressor, unit status, an alarm status, voltage output, and voltage input.
Cella, however, teaches wherein the technical parameters of the compressor include a system pressure, an ambient temperature, a compressor discharge temperature a line pressure, a speed of a variable speed drive, a dew point, a carbon monoxide level, a carbon dioxide level, an operating mode of a component of the compressor, a running time of the component, average daily runtime of the compressor, unit status, an alarm status, voltage output, and voltage input (Cella: abstract; ¶¶ [0316], [0460], [0398], [1341], [1762], [2004]).
The motivation to include the teachings of Cella with the teachings of Jain is the same as that of claim 1 above and is incorporated herein.
CLAIM 11
Jain teaches the computer-implemented method of claim 1, wherein the one or more data packets are received at predetermined intervals, and represent a new state of the compressor (Jain: abstract; ¶¶ [0057] “data packets may be sent at the user's request or at selected time intervals programmed in the system”).
CLAIM 12
Jain does not appear to explicitly teach the computer-implemented method of claim 1, further comprising: reconstructing, by the one or more processors, a text of the digital report so that the information is presented as continuous natural language that can be parsed by conventional natural language processing algorithms and machine reading and comprehension engines.
Cella, however, teaches further comprising: reconstructing, by the one or more processors, a text of the digital report so that the information is presented as continuous natural language that can be parsed by conventional natural language processing algorithms and machine reading and comprehension engines (Cella: abstract; ¶¶ [0397] “machine learning techniques and systems may be used in…Natural Language Processing (NLP)”, [0410] “data market search system 4118, which may include features that enable a user to indicate what types of data a user wishes to obtain, such as by entering keywords in a natural language search interface that characterize data or metadata”).
CLAIM 13
Jain teaches a computer program product (Jain: abstract; ¶¶ [0066]).
The remainder of claim 13 repeats substantially the same limitations as those in claim 1. As such, the remainder of claim 13 is rejected for substantially the same reasons given for claim 1 and are incorporated herein.
CLAIM 14
Jain teaches one or more processors (Jain: abstract; ¶¶ [0035]); one or more computer readable storage media (Jain: abstract; ¶¶ [0066]); and computer readable code stored collectively in the one or more computer readable storage media, with the computer readable code including data and instructions to cause the one or more computer processors to perform at least the following operations (Jain: abstract; ¶¶ [0066]).
The remainder of claim 14 repeats substantially the same limitations as those in claim 1. As such, the remainder of claim 14 is rejected for substantially the same reasons given for claim 1 and are incorporated herein.
CLAIMS 15-18
Claims 15-18 repeat substantially the same limitations as those in claims 2-5. As such, claims 15-18 are rejected for substantially the same reasons given for claims 2-5 and are incorporated herein.
3.2. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jain (US 2011/0166628), in view of Cella et al. (US 2019/0339688), and further in view of Muhsin et al. (US 2018/0317826).
CLAIM 10
Jain does not appear to explicitly teach the computer-implemented method of claim 1, wherein the data contained in the one or more data packets are key-value pairs, indicating a key associated with a technical parameter and a value associated with the technical parameter.
Muhsin, however, teaches wherein the data contained in the one or more data packets are key-value pairs, indicating a key associated with a technical parameter and a value associated with the technical parameter (Muhsin: abstract; ¶¶ [0335] “message format may, for instance, include key-value pairs (sometimes called tag-value pairs) where keys define types or categories of data (such as parameter types, alarms, and so on) and the values are instances of the types or categories (such as specific parameter values, alarm states, and so on); [0362]-[0364] “medical network interface 7710 can be connected to the patient monitor 7701 and can receive packets from a pump. The packets can indicate a device ID for the pump”; FIGS. 1-83).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system for displaying and controlling medical monitoring data, as taught by Muhsin, with the method and system for monitoring and maintenance of equipment such as compressors, as taught by Cella, with the system and method and device for medical device processing and management, as taught by Jain, with the motivation of facilitating communication of information (Muhsin: ¶¶ [0194]-[0197]).
Allowable Subject Matter
4. Claims 6-7 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Tomaszewski whose telephone number is (313)446-4863. The examiner can normally be reached M-F 5:30 am - 2:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL TOMASZEWSKI/Primary Examiner, Art Unit 3681
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