DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-20 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.
Regarding claims 1, 10, and 13, the claims recite “determine a quantum of icing and a rate of ice formation in the one or more refrigeration units based on the identified frosting pattern” which renders the claim indefinite as it is unclear what is meant by “a quantum of icing” and what constitutes a quantum of icing. For the purposes of examination, the Examiner will interpret the claim to mean any formation of ice at all. Clarification is requested.
Claims 7 and 17 recite the limitation " the non-systematic events.” There is insufficient antecedent basis for this limitation in the claim.
Claims 2-9 are rejected based on their dependency to claim 1.
Claims 11-12 are rejected based on their dependency to claim 10.
Claims 14-20 are rejected based on their dependency to claim 13.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6-8, 10-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kang (US 2021/0333039 – provided by Applicant in the IDS) in view of FOR1 (C106482408A).
Regarding claim 1, Kang teaches a system to predict ice gradient in one or more refrigeration units (see Title, see paragraphs [0050]-[0055]), the system comprising:
a server (700, Fig. 4) in communication with the one or more refrigeration units (50a, 50b, 50c, Fig. 4),
the server comprising a processor (700, Fig. 4) coupled to a memory storing instructions executable by the processor (see paragraph [0064]) and configured to:
receive data pertaining to one or more parameters of the one or more refrigeration units (see paragraph [0068] at least);
correlate the received parameters to generate one or more characteristic features indicative of behaviour and performance of one or more components of the one or more refrigeration units (see paragraph [0069] which notes diagnosing a current state of the unit);
generate one or more events based on the generated characteristic features (see paragraph [0070], which notes a frost start timing predictor which correlates to an event as the prediction constitutes an event);
identify a frosting pattern associated with the one or more refrigeration units by analyzing the one or more events (see paragraph [0070] which notes using machine learning to predictor frosting patterns);
determine a quantum of icing and a rate of ice formation in the one or more refrigeration units based on the identified frosting pattern for each of the one or more refrigeration units (see claim 2 which notes predicting a time until frosting, further see paragraph [0071] which notes the time until frosting includes time for the frosted state to be determine at a threshold value, which is akin and analogous to a rate of ice formation as it is determining the level of frost over a period of time until it reaches a frosted state, per the quantum of icing, the Examiner has interpreted this to be related to any formation of a layer of frost on the heat exchanger).
Kang does not teach:
predict ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation.
FOR1 teaches a heat pump defrosting control (FOR1, Title) which features a prediction model that determines the rate of frosting over different frosting surfaces (FOR1, see Description, “prediction model and a linear fitting, the frosting area, the rate of the same point fitting, connected to form four curves, the frosting area from the top down is refined to (A), (B), (C), (D), (E) five regions when the air source heat pump unit is operated in each frost region, the frosting speed of outdoor heat exchanger are similar, and gradually reduced from the top down (A), (B), (C), (D), (E), five frosting area of frosting rate; when the machine set runs in the frosting area (A)”), wherein the prediction model uses a linear fitting to determine the pattern of frosting (FOR1, Description, “prediction model to simulate the frosting speed of cold surface under different condition analysis in frost region, using linear fitting, the frosting velocity the same point fitting into curve, forming frosting rate curves A, B, C, D, and obtaining the specific equation. finally the scene actually measuring the correctness of the verification pattern” The Examiner notes that the determination of the predicting the ice gradient is met by FOR1 as the definition of a gradient is the magnitude of a property in passing between one point to another, which the linear model measures).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Kang with predicting the ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation, as taught by FOR1, in order to utilize a comprehensive predicting pattern to set defrosting parameters (FOR1, Description).
Regarding claim 2, Kang as modified teaches the system of claim 1, wherein the server is configured to generate and transmit a set of alert signals to the one or more refrigeration units and/or one or more mobile devices based on the predicted ice gradient (met through the combination, see Kang 1041, Fig. 10, paragraph [0133] which notes that when frost is determined signals are sent to initiate defrosting control, the prediction of the ice gradient occurs in conjunction with defrosting control, met through the combination with FOR1).
Regarding claim 3, Kang as modified teaches the system of claim 1, wherein one or more mobile devices are in communication with the server and/or the one or more refrigeration units (Kang, 40, Fig. 1, paragraph [0028]).
Regarding claim 4, Kang as modified teaches the system of claim 1, wherein the server is configured to: classify the one or more generated events into a systematic event and a non-systematic event (Kang, paragraph [0069], the state of the air conditioner is akin to classifying a systematic event and a non-systematic event, the Examiner is interpreting a systematic event as the state being any operation of the air conditioner outside of requiring defrost, and a non-systematic event as one which requires initiating defrost control logic); and identify a frosting pattern associated with the one or more refrigeration units by analyzing the one or more non-systematic events (Kang, paragraphs [0069]-[0072]).
Regarding claim 6, Kang as modified teaches the system of claim 1, wherein the server is configured with an ancillary injection module to determine unavailable parameters associated with a set of refrigeration units among the one or more refrigeration units based on the available parameters associated with another set of refrigeration units among the one or more refrigeration units (Kang, paragraphs [0057]-[0060] wherein the server is described to determine which air conditioner needs to be defrosted and those which does not, the unavailable and available parameters are defined to be the need for defrosting or not).
Regarding claim 7, Kang as modified teaches the system of claim 1, wherein the server is configured to identify the frosting pattern from the non-systematic events by normalizing the identified non-systematic events that are not associated with frosting, wherein the identification, normalization, and differentiation of the systematic events and the non-systematic events are achieved by continuous learning during the ice gradient prediction, and wherein the non-systematic events are analyzed based on one or more of a trend, a recent performance, and a quantum of variation of the corresponding non-systematic events (met through the combination as Kang teaches the identification in at least paragraphs [0059]-[0060], while the normalization and differentiation lie in the analysis and acknowledgement of frosting described in [0060] regarding the processing of data and smoothing out the images of the outdoor heat exchanger, while FOR1 teaches the prediction model analysis utilizes a linear fitting model which is analogous to a trend).
Regarding claim 8, Kang as modified teaches the system of claim 1, wherein the server is in communication with a controller associated with the one or more refrigeration units, wherein the controller is configured to: monitor and store the one or more parameters of the corresponding refrigeration unit (Kang, paragraph [0027]).
Regarding claim 10, Kang teaches a device configurable with one or more refrigeration units and operable to predict ice gradient in the one or more refrigeration units (see Title, see paragraphs [0050]-[0055]), the device comprising:
a processing unit (700, Fig. 4) adapted to be operatively coupled to the one or more refrigeration units, the processing unit comprising a processor coupled to a memory storing instructions executable by the processor (see paragraph [0064]) and configured to:
capture data pertaining to one or more parameters of the one or more refrigeration units (see paragraph [0068] at least);
correlate the captured parameters to generate one or more characteristic features indicative of behaviour and performance of one or more components of the one or more refrigeration units (see paragraph [0069] which notes diagnosing a current state of the unit);
generate one or more events based on the generated characteristic features; classify the one or more generated events into a systematic event and a non-systematic event (see paragraph [0070], which notes a frost start timing predictor which correlates to an event as the prediction constitutes an event, see paragraph [0069], the state of the air conditioner is akin to classifying a systematic event and a non-systematic event, the Examiner is interpreting a systematic event as the state being any operation of the air conditioner outside of requiring defrost, and a non-systematic event as one which requires initiating defrost control logic);
identify a frosting pattern associated with the one or more refrigeration units by analyzing the one or more non-systematic events (see paragraph [0070] which notes using machine learning to predictor frosting patterns);
determine a quantum of icing and a rate of ice formation in the one or more refrigeration units based on the identified frosting pattern for each of the one or more refrigeration units (see claim 2 which notes predicting a time until frosting, further see paragraph [0071] which notes the time until frosting includes time for the frosted state to be determine at a threshold value, which is akin and analogous to a rate of ice formation as it is determining the level of frost over a period of time until it reaches a frosted state, per the quantum of icing, the Examiner has interpreted this to be related to any formation of a layer of frost on the heat exchanger).
Kang does not teach:
predict ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation.
FOR1 teaches a heat pump defrosting control (FOR1, Title) which features a prediction model that determines the rate of frosting over different frosting surfaces (FOR1, see Description, “prediction model and a linear fitting, the frosting area, the rate of the same point fitting, connected to form four curves, the frosting area from the top down is refined to (A), (B), (C), (D), (E) five regions when the air source heat pump unit is operated in each frost region, the frosting speed of outdoor heat exchanger are similar, and gradually reduced from the top down (A), (B), (C), (D), (E), five frosting area of frosting rate; when the machine set runs in the frosting area (A)”), wherein the prediction model uses a linear fitting to determine the pattern of frosting (FOR1, Description, “prediction model to simulate the frosting speed of cold surface under different condition analysis in frost region, using linear fitting, the frosting velocity the same point fitting into curve, forming frosting rate curves A, B, C, D, and obtaining the specific equation. finally the scene actually measuring the correctness of the verification pattern” The Examiner notes that the determination of the predicting the ice gradient is met by FOR1 as the definition of a gradient is the magnitude of a property in passing between one point to another, which the linear model measures).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Kang with predicting the ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation, as taught by FOR1, in order to utilize a comprehensive predicting pattern to set defrosting parameters (FOR1, Description).
Regarding claim 11, Kang as modified teaches the device of claim 10, wherein the device comprises a set of sensors to capture and monitor the one or more parameters of the one or more refrigeration units (Kang, 350, Fig. 3, paragraph [0058]).
Regarding claim 12, Kang as modified teaches the device of claim 10, wherein the device is configured transmit a set of control signals to a defrosting unit of the one or more refrigeration units to control defrosting of the corresponding refrigeration unit based on the predicted ice gradient (met through the combination, see Kang 1041, Fig. 10, paragraph [0133] which notes that when frost is determined signals are sent to initiate defrosting control, the prediction of the ice gradient occurs in conjunction with defrosting control, met through the combination with FOR1).
13. A method for predicting ice gradient in one or more refrigeration units (see Title, see paragraphs [0050]-[0055]), the method comprising the steps of:
capturing data pertaining to one or more parameters of the one or more refrigeration units (see paragraph [0068] at least);
correlating the received parameters to generate one or more characteristic features indicative of behaviour and performance of one or more components of the one or more refrigeration units (see paragraph [0069] which notes diagnosing a current state of the unit);
generating one or more events based on the generated characteristic features; identifying a frosting pattern associated with the one or more refrigeration units by analyzing the one or more events (see paragraph [0070], which notes a frost start timing predictor which correlates to an event as the prediction constitutes an event);
determining a quantum of icing and a rate of ice formation in the one or more refrigeration units based on the identified frosting pattern (see claim 2 which notes predicting a time until frosting, further see paragraph [0071] which notes the time until frosting includes time for the frosted state to be determine at a threshold value, which is akin and analogous to a rate of ice formation as it is determining the level of frost over a period of time until it reaches a frosted state, per the quantum of icing, the Examiner has interpreted this to be related to any formation of a layer of frost on the heat exchanger).
Kang does not teach predicting ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation.
FOR1 teaches a heat pump defrosting control (FOR1, Title) which features a prediction model that determines the rate of frosting over different frosting surfaces (FOR1, see Description, “prediction model and a linear fitting, the frosting area, the rate of the same point fitting, connected to form four curves, the frosting area from the top down is refined to (A), (B), (C), (D), (E) five regions when the air source heat pump unit is operated in each frost region, the frosting speed of outdoor heat exchanger are similar, and gradually reduced from the top down (A), (B), (C), (D), (E), five frosting area of frosting rate; when the machine set runs in the frosting area (A)”), wherein the prediction model uses a linear fitting to determine the pattern of frosting (FOR1, Description, “prediction model to simulate the frosting speed of cold surface under different condition analysis in frost region, using linear fitting, the frosting velocity the same point fitting into curve, forming frosting rate curves A, B, C, D, and obtaining the specific equation. finally the scene actually measuring the correctness of the verification pattern” The Examiner notes that the determination of the predicting the ice gradient is met by FOR1 as the definition of a gradient is the magnitude of a property in passing between one point to another, which the linear model measures).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Kang with predicting the ice gradient associated with the one or more refrigeration units based on the determined quantum of icing and the rate of ice formation, as taught by FOR1, in order to utilize a comprehensive predicting pattern to set defrosting parameters (FOR1, Description).
Regarding claim 14, Kang as modified teaches the method of claim 13, wherein the method comprises the step of transmitting a set of control signals to a defrosting unit of the one or more refrigeration units to control defrosting of the corresponding refrigeration unit based on the predicted ice gradient (met through the combination, see Kang 1041, Fig. 10, paragraph [0133] which notes that when frost is determined signals are sent to initiate defrosting control, the prediction of the ice gradient occurs in conjunction with defrosting control, met through the combination with FOR1)..
Regarding claim 15, Kang as modified teaches the method of claim 13, wherein the method comprises the step of determining unavailable parameters associated with a set of refrigeration units among the one or more refrigeration units based on the available parameters captured from another set of refrigeration units among the one or more refrigeration units (Kang, paragraphs [0057]-[0060] wherein the server is described to determine which air conditioner needs to be defrosted and those which does not, the unavailable and available parameters are defined to be the need for defrosting or not).
Regarding claim 16, Kang as modified teaches the method of claim 13, wherein the method comprises the steps of: classifying the one or more generated events into a systematic event and a non-systematic event; and identifying a frosting pattern associated with the one or more refrigeration units by analyzing the one or more non-systematic events (Kang, paragraph [0069], the state of the air conditioner is akin to classifying a systematic event and a non-systematic event, the Examiner is interpreting a systematic event as the state being any operation of the air conditioner outside of requiring defrost, and a non-systematic event as one which requires initiating defrost control logic); and identify a frosting pattern associated with the one or more refrigeration units by analyzing the one or more non-systematic events (Kang, paragraphs [0069]-[0072]).
Regarding claim 17, Kang as modified teaches the method of claim 13, wherein the method comprises the step of normalizing the identified non-systematic events that are not associated with frosting to identify the frosting pattern from the non-systematic events, and wherein the non-systematic events are analyzed based on a trend, a recent performance, and a quantum of variation of the corresponding non-systematic events (met through the combination as Kang teaches the identification in at least paragraphs [0059]-[0060], while the normalization and differentiation lie in the analysis and acknowledgement of frosting described in [0060] regarding the processing of data and smoothing out the images of the outdoor heat exchanger, while FOR1 teaches the prediction model analysis utilizes a linear fitting model which is analogous to a trend).
Regarding claim 19, Kang as modified teaches the method of claim 13, wherein the one or more parameters comprises: dynamic parameters comprising one or more of evaporator surface temperature (Kang, see paragraph [0117], [0115]), return air temperature, defrost status, and expansion valve opening degree (the remainder limitations are not required as the claim is claimed in the alternative); and
static parameters comprising one or more of: cooling type of the one or more refrigeration unit (Kang, paragraph [0126]), type of controller used in the corresponding refrigeration unit; attributes comprising type (the remainder limitations are not required as the claim is claimed in the alternative);
and operational policies associated with the one or more refrigeration units (Kang, paragraphs [0123]-[0130]).
Regarding claim 20, Kang as modified teaches the method of claim 13, wherein the one or more characteristic features comprise one or more of defrost characteristics (Kang, paragraph [0069] at least), heat exchange characteristics, return air temperature characteristics, and expansion valve characteristics (the remainder limitations are not required as the claim is claimed in the alternative).
Allowable Subject Matter
Claims 5, 9, and 18 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.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art of record is Kang (US 2021/0333039 – provided by Applicant in the IDS) in view of FOR1 (CN105716340A).
The prior art of record when considered as a whole, either alone or in combination, does not anticipate or render obvious:
Regarding claim 5:
wherein the server is configured with a machine learning module that is configured to: update a database associated with the server with a set of data packets comprising one or more of the captured parameters, the generated characteristic features, the identified non-systematic events and corresponding predefined threshold values, the identified frosting pattern, the determined quantum of icing and rate of ice formation, and the predicted ice gradient; and train, based on the updated database, the server to predict the ice gradient of the one or more refrigeration units in real-time.
In the Examiner’s opinion, it would not be obvious to further modify the prior art structures to arrive at the claimed invention, absent impermissible hindsight. Therefore, rendering claim 5, with dependent claims therefrom are considered allowable.
Regarding claim 9:
when the predicted ice gradient exceeds a threshold value, the server is configured to transmit a set of alert signals to a defrosting unit of the one or more refrigeration units via the controller to enable defrosting of the corresponding refrigeration unit, wherein the threshold value is a dynamic value that is determined and updated by continuously learning during the ice gradient prediction.
In the Examiner’s opinion, it would not be obvious to further modify the prior art structures to arrive at the claimed invention, absent impermissible hindsight. Therefore, rendering claim 9, with dependent claims therefrom are considered allowable.
Regarding claim 18:
when the predicted ice gradient exceeds a threshold value, the method comprises the step of transmitting a set of alert signals to the defrosting unit of the one or more refrigeration units to enable defrosting of the corresponding refrigeration unit, wherein the threshold value is a dynamic value that is determined and updated by continuously learning during the ice gradient prediction.
In the Examiner’s opinion, it would not be obvious to further modify the prior art structures to arrive at the claimed invention, absent impermissible hindsight. Therefore, rendering claim 18, with dependent claims therefrom are considered allowable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAEL N BABAA whose telephone number is (571)270-3272. The examiner can normally be reached M-F, 9-5 EST.
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/NAEL N BABAA/Primary Examiner, Art Unit 3763