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 .
Claims 44-51 are pending in the application.
Claims 44-50 are elected.
Claim 51 is withdrawn from consideration.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below 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 claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Election/Restrictions
Applicant’s election without traverse of group I claims 44-50 in the reply filed on 11/18/2025 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/07/2023, 05/10/2024, 02/19/2025, 08/22/2025, 12/01/2025 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 44-45, 48-50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kokugan Yoko et al. WO 2018163402 A1 (“Yoko”) in view of Guinard et al. US Pub. No. 2019/0190739 (“Guinard”).
Regarding claim 44, Yoko teaches an asset management system for a refrigerator 3],
a operational data monitor 2], 3] and comprising a modem configured for network communication [SEE network symbol in fig. 1] and one or more I/O ports configured for wired connection to the respective refrigeration appliance [SEE direct connection between 2 and 3 in fig. 1];
[0014] The air conditioner performance diagnostic device and performance diagnostic method of the present invention are suitable as a technique for monitoring an air conditioner from a
remote location.
[0021] A performance evaluation server 1 (performance diagnostic device) for the chiller 3 is
connected to the chiller 3 via an operation data monitor 2 which is a transmitter. The operational data acquired by the operational data monitor 2 includes signals from sensors provided in the refrigerator 3, and includes raw data obtained from the refrigerator 3 that is actually operating. In this embodiment, the chiller 3 is assumed to be a turbo chiller, the configuration of which will be described in detail later with reference to FIG.
an asset manager [evaluation server 1] configured to receive operating data from source refrigeration appliances transmitted via the modems of the
[0021] A performance evaluation server 1 (performance diagnostic device) for the chiller 3 is
connected to the chiller 3 via an operation data monitor 2 which is a transmitter. The operational data acquired by the operational data monitor 2 includes signals from sensors provided in the refrigerator 3, and includes raw data obtained from the refrigerator 3 that is actually operating. In this embodiment, the chiller 3 is assumed to be a turbo chiller, the configuration of which will be described in detail later with reference to FIG.
[0022] The performance evaluation server 1 is provided with a main memory device 10 (first memory unit), a secondary memory device 11 (second memory unit), an interface 12, a CPU 13 (central processing unit), an input device 14 (input unit), and an output device 15 (output unit), and diagnoses performance changes of the refrigerator 3. The main memory device 10 comprises a data collection section 10A, a reference data creation section 10B, a system performance evaluation section 10C (performance evaluation section), and an output section 10D. The first storage unit and the second storage unit can be collectively referred to simply as "storage units."
[0023] The data collection unit 10A has a function of measuring data corresponding to desired evaluation parameters via a sensor provided in the refrigerator 3, and a function of recording the measured time-series data as historical data.
an OEM database containing proprietary OEM data
[0032] The equipment characteristics database is a collection of data that covers all the operating conditions that satisfy the specifications of the chiller. The equipment characteristics database may be compiled using the design values of the refrigerator, the results of quality confirmation tests performed using a test machine before shipping to be included in catalogs issued by refrigerator manufacturers, etc.
wherein the asset manager is configured to read the proprietary OEM data from the OEM database and wherein the asset manager is configured to act on the operating data for at least one refrigeration appliance of a specified refrigeration appliance type by combining the operating data for said at least one refrigeration appliance of the specified refrigeration appliance type with the proprietary OEM data for said specified refrigeration appliance type [SEE further fig. 8].
[0046] First, in S100, evaluation parameters input from the input device 14 are acquired, and normal data is also acquired from the data collection unit 10A. In this embodiment, the evaluation parameters are the load factor, the COP, and the cooling water inlet temperature.
[0048] Next, in S101, in order to evaluate the system performance, the load factor and cooling
water inlet temperature other than the COP corresponding to the system performance of the
chiller 3 among the evaluation parameters are set as operating conditions, and the normal
data is classified according to the operating conditions.
[0049] Thereafter, in S102, the device characteristic database is acquired from the secondary storage device 11. Then, in S103, a correction coefficient for matching the normal data with the device characteristic data for each operating condition is calculated. Depending on the operating conditions of the refrigerator 3, there may be no normal data, but the correction coefficients for the parts where the operating conditions match are interpolated or extrapolated to calculate the correction coefficients over the entire operating range in the equipment characteristics database.
[0050] Finally, in S104, each piece of data in the equipment characteristic database is multiplied by the corresponding correction coefficient for each operating condition to create individual
characteristic curve data representing the undegraded system performance of the actually
installed chiller 3. This data is not only a group of data similar to those in the equipment characteristics database, but is also output from the output section 10D of the main memory device 10 as a three-dimensional graph with the load factor, COP, and cooling water inlet temperature, which are evaluation parameters, on the X, Y, and Z axes, respectively, and is displayed on the operation data monitor 2 via the output device 15.
In summary, Yoko teaches a remote server equipped with a data collection unit that collects and records refrigerator operational data via an IOT devices, and a device characteristic database, which is a data group encompassing operation conditions satisfying the specifications of the refrigerator, and further equipped with a reference data creation unit that uses the device characteristic database and the operational data possessed by the data collection unit to calculate individual characteristic curve data, and a performance evaluation unit that evaluates the performance of the air conditioner by comparing a portion of the operational data possessed by the data collection unit and the individual characteristic curve data.
Yoko does not expressly teach a plurality of refrigeration appliances, each the refrigeration appliance being one of a plurality of different refrigeration appliance types, a plurality of IOT devices, each the IOI device being bound to a respective refrigeration appliance and an OEM database containing proprietary OEM data organized by refrigeration appliance type.
Guinard teaches another asset management system for a plurality of appliances [110 appliance – SEE fig. 1], an asset manager configured [Cloud Platform 104] to receive operation data from sources appliances transmitted via the modems of the plurality of IOT devices [Sensing Module 102; SEE par. 0145, 0147, 0150]. Specifically, Guinard teaches the asset management system comprises a plurality of appliances, each the appliance being one of a plurality of different appliance types, a plurality of IOT devices, each the IOI device being bound to a respective appliance, and an OEM database containing proprietary OEM data organized by appliance type.
[0122] With reference to FIG. 1A, preferred embodiments of the system 100 may include one or more sensing modules 102, a cloud platform 104, and software system or platform 106.
[0126] Although only one sensing module is shown in the drawing in FIGS. 1A-1C, it should be appreciated that a system 100 may include multiple sensing modules associated with multiple appliances. A particular sensing module may be associated with one or more appliances, and a particular appliance may have more than one sensing module associated therewith. FIGS. 3A-3C show exemplary configurations of sensing modules and appliances.
[0147] A sensing module 102 may be configured to continually monitor the appliance 110 or it may be configured to periodically monitor the appliance 110, e.g., on a predetermined frequency or schedule. Alternatively, a sensing module 102 may be configured to switch from periodic monitoring mode to a continual monitoring mode upon the sensing of a particular output or type of output from the appliance 110 that may indicate a potential problem with the appliance 110 (e.g., a particular sound or high intensity vibration). In any event, sensing module 102 may sense information from the appliance 110 that it may then transmit to the cloud platform 104 and/or the local controller 108, or that it may process using its controller 200. The sensing module 102 may be configured to continually send or otherwise transmit all of the data that the sensing module 102 may collect from the appliance 110 to the cloud platform 104 and/or the local controller 108. Alternatively, in some exemplary embodiments, the module 102 may have the ability to parameterize, categorize, manipulate, filter, or otherwise process the sensed data on the module 102 prior to sending the data to the cloud platform 104 and/or local controller 108. In this way, the module 102 may determine what data may have a higher probability of representing a need for an intervention and may send or otherwise transmit this categorized data to the cloud platform 104 and/or the local controller 108 for analysis. Once sent to the cloud platform 104 and/or local controller 108, the data may be analyzed, e.g., as described herein.
[0166] The software system/platform 106 may be installed and run on the cloud platform 104
[0167] With reference to FIG. 4, a software system/platform 106 according to exemplary embodiments hereof may include a service monitoring and prediction module 400
[0168] The service monitoring and prediction module 400 may include an appliance models module 404, a business rules module 406, a machine-learning (ML) module 408, and other types of modules that may assist in the analysis of appliance data.
[0169] The appliance models module 404 may include stored models of operation for each appliance 110 (or type of appliance) that may be used to determine the current condition of the appliance 110 and to predict when the appliance 110 may require an intervention. The models of operation may include documented outputs for each individual appliance 110 that may be compared to the sensed outputs received from the sensing modules 102. In one example, the sensed information about a particular appliance (e.g., a sensed vibration intensity level) may be compared to the expected outputs of the appliance model (e.g., a documented expected vibration intensity level) to determine if the sensed information falls within the expected model of operation or if the sensed output indicates that the appliance 110 may be operating outside the expected model. If the comparison of sensed data to expected data indicates that the appliance may have a problem or may require an intervention as described above, software platform 106 may initiate the determined intervention. In another example, the models of operation for a particular appliance 110 may be used to track the wear of a particular component within the appliance that may have a predictable life cycle. In this way, the monitoring and prediction module 400 may predict when the component may be approaching the end of its life cycle, and may initiate an intervention to have it replaced before it does.
[0170] The appliance models may also include other information regarding the appliances 110 such as the type or general classification of each appliance 110, the various properties that may be monitored by sensing modules 102 (i.e. property models), the property values that may be expected (including data types, data ranges, etc.) and other types of information. The property models may define the data types that may be sensed by sensing modules 102 and analyzed by software platform 106. It may be preferable and important for each appliance model of operation to be designed generally to represent each appliance 110 such that each appliance 110 may generally conform to its respective appliance model during normal operation.
[0171] The models of operation may be developed, determined or otherwise created by the manufacturer of the particular appliance 110 during the design, prototyping, manufacturing and quality assurance stages or during any other time in the life cycle of the appliance 110. The models may be based on empirical data or on theoretical data derived from design models of the appliances 110. In any case, a manufacturer may determine and otherwise provide a preferably comprehensive model of operation for each appliance 110 that may be used to classify, categorize, catalog, or otherwise be compared to actual sensed appliance data provided to the software platform 106 by the sensing modules 102.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Yoko with the asset management system comprises a plurality of appliances, each the appliance being one of a plurality of different appliance types, a plurality of IOT devices, each the IOI device being bound to a respective appliance, and an OEM database containing proprietary OEM data organized by appliance type of Guinard. The motivation for doing so would has been to allow the asset manage the ability to evaluate the condition of different type of refrigeration appliance, using accurate baseline characteristic data supplied by different OEM for a specific type of refrigeration appliance. Thus, reduce maintenance cost, improve service efficiency.
Regarding claim 45, Yoko teaches the asset manager is configured to act on the operating data by generating one of a predictive analytic output and a simulation output for said at least one refrigeration appliance of the specified refrigeration appliance type based on the operating data for said at least one refrigeration appliance of the specified refrigeration appliance type and the proprietary OEM data for said specified refrigeration appliance type [SEE fig. 6 and 7].
Regarding claim 46, Yoko teaches the proprietary OEM data includes a model for said specified refrigeration appliance type correlating liquid line temperature to degradation of compressor operating efficiency [par. 0032, 0073, 0082, 0085], wherein the operating data for said at least one refrigeration appliance of the specified refrigeration appliance type includes liquid line temperature for said at least one refrigeration appliance of the specified refrigeration appliance type [0027], and wherein the asset manager is configured to act on the operating data by predicting compressor failure based on the liquid line temperature for said at least one refrigeration appliance of the specified refrigeration appliance type and the model.
Regarding claim 48, Yoko teaches the proprietary OEM data includes a three-dimensional temperature model for said specified refrigeration appliance type correlating operating data to temperature throughout the refrigeration appliance, and wherein the asset manager is configured to act on the operating data by making a three-dimensional temperature simulation of temperatures throughout the refrigeration appliance based on the operating data and the three-dimensional temperature model [SEE fig. 6 and par. 0032].
Regarding claim 49, Yoko in view of Guinard teaches each refrigeration appliance has a unique serial number and the asset manager is configured to parse the operating data by the serial number of the source refrigeration appliance [SEE par. 0156 of Guinard].
Regarding claim 50, Yoko teaches the asset manager is configured to store the operating data in a time series database [A of data collection parts are provided with the function to measure the data corresponding to a desired evaluation parameter via the sensor provided in the refrigerator 3, and the function to record the measured time series data] and Guinard teaches [the cloud platform 104 may receive data from and/or transmit data to one or more sensor modules 102 at a time, simultaneously and in real time…. It may also be preferable that each appliance 110 also has a unique identifier such as a serial number and that the cloud platform may recognize each unique appliance 110 identifier. In this way, the cloud platform 104 may organize and manage the data for each sensing module 102 and appliance 110, identify the exact appliances 110 that may require an intervention, and may schedule, initiate and generally execute the intervention accordingly - 0156]. Therefore, it is obvious to one of ordinary skill in the art Yoko in view of Guinard teaches the asset manager is configured to store the operating data in a time series database using the serial numbers of the source refrigeration appliances as primary keys.
Claim(s) 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoko/Guinard as applied to claim 44 above, and further in view of Shockley et al. US Pub. No. 2018/0017301 (“Shockley”).
Regarding claim 46, Yoko in view of Guinard teaches the proprietary OEM data includes a model for said specified refrigeration appliance type correlating liquid line temperature to degradation of manager is configure to act on the operating data by predicting performance degradation based on the liquid line temperature for said at least one refrigeration appliance of the specified refrigeration appliance type and the model [SEE par. 0007, 00035, 0069, 0072-0073, 0082 of Yoko].
Yoko in view of Guinard does not teach correlating liquid line temperature to degradation of compressor operating efficiency and wherein the asset manager is configured to act on the operating data by predicting compressor failure based on the liquid line temperature for said at least one refrigeration appliance.
Shockley teaches a refrigeration system monitor, comprising a computing device with a memory storing instructions executable by a processor to: monitor real time performance metrics for a refrigeration system, wherein the real time performance metrics are received from temperature sensors, pressure sensors, and power sensors coupled to the refrigeration system, compare the real time performance metrics to a performance curve of devices associated with the refrigeration system, and generate health information for the devices associated with the refrigeration system based on the comparison. Specifically, Shockley teaches a proprietary OEM data includes a model for said specified refrigeration appliance type correlating liquid line temperature to degradation of compressor operating efficiency [SEE par. 0037, 0039] and wherein the asset manager is configured to act on the operating data by predicting compressor failure based on the liquid line temperature for said at least one refrigeration appliance [par. 0031, 0035, 0040-0042].
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Yoko/Guinard with the proprietary OEM data includes a model for said specified refrigeration appliance type correlating liquid line temperature to degradation of compressor operating efficiency and wherein the asset manager is configured to act on the operating data by predicting compressor failure based on the liquid line temperature for said at least one refrigeration appliance of Shockley. The motivation for doing so would has been to prevent unexpected failure of a compressor of the refrigeration appliance. Thus, avoid costly fix.
Allowable Subject Matter
Claim 47 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.
The following is a statement of reasons for the indication of allowable subject matter: Claim 7 is considered allowable since, when reading the claims in light of the specification, none of the references of record alone or in combination disclose or suggest the combination of subject matter specified in the dependent claim(s): the model further correlates air temperature to degradation of compressor operating efficiency,… the asset manager is configured to act on the operating data by predicting compressor failure based on the liquid line temperature and the return air temperature for said at least one refrigeration appliance of the specified refrigeration appliance type and the model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KR 20190093010 to Kim Bok Han teaches a wireless IoT-based refrigerator malfunction monitoring device comprises: a power supply unit supplying or blocking power to a refrigerator; a communication unit communicating with an external management server; a power detection unit detecting a current or voltage of the refrigerator; and a control unit obtaining power information of the refrigerator at a preset time schedule to transmit the power information to the management server through the communication unit, and receiving a control signal from the management server to block the power of the refrigerator through the power supply unit, if the power information is out of a preset range. Accordingly, the amount of power of the refrigerator is monitored and failure diagnosis can be quickly performed.
PNG
media_image1.png
228
355
media_image1.png
Greyscale
US Pub. No. 2016/0299038 to Liu et al. teach a detector configured to sample power consumption to obtain a power consumption time series having resolution sufficient to extract power cycle information for one or more individual components of a thermostat-controlled cycling appliance; and an analyzer configured to extract the power cycle information from the sampled power consumption time series, to compare the power cycle information to stored information of known power cycle characteristics and to classify technology of the one or more components of the appliance based on comparison of the power cycle information to the stored information, the analyzer being further configured to generate an output that indicates the technology.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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, Kamini S Shah can be reached at 571-272-2279. 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.
VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115