DETAILED ACTION
1. Claims 1-20 have been 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
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.
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 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.
3. Claims 1, 3, 5, 11, 13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by U.S. Patent Publication No. 20220134116, hereafter Fried.
Regarding Claim 1: The reference discloses A method for generating simulations based on data, the method comprising:
receiving internal data and external data which affect output data; (“[0036] The system may estimate the temperature of the external portion of the device housing using an algorithm that incorporates the one or more temperature measurements corresponding to the internal portion of the device. In general, the temperature estimation algorithm represents a temperature difference between an internal portion of the device and the external portion of the device based on a predetermined dynamic transfer function that operates in the time-domain. Where the device includes multiple temperature sensors, a unique transfer function may be determined for each temperature sensor of the device, where the transfer function is based on a thermal resistance that may be relatively unique for each temperature sensor of the device.” See also “[0072] In some examples, IMD 14 may include a single temperature sensor. In another example, IMD 14 may include a plurality of temperature sensors. In an example, charging head 26 (e.g., external of patient 12) and/or IMD 14 (e.g., implanted within patient 12) may each include one or more temperature sensors.”)
differencing the internal data, the external data, and the output data to create first differenced data; (“[0036] The system may estimate the temperature of the external portion of the device housing using an algorithm that incorporates the one or more temperature measurements corresponding to the internal portion of the device. In general, the temperature estimation algorithm represents a temperature difference between an internal portion of the device and the external portion of the device based on a predetermined dynamic transfer function that operates in the time-domain. Where the device includes multiple temperature sensors, a unique transfer function may be determined for each temperature sensor of the device, where the transfer function is based on a thermal resistance that may be relatively unique for each temperature sensor of the device.”)
training a machine learning model using the first differenced data; ([0116] “The thermodynamics information may be learned over time based on data obtained via IMD 14 and/or external charging device 22, such as by using a machine learning (ML) or artificial intelligence (AI) algorithm to train IMD 14 or external charging device 22 to better understand the thermodynamics of system 10.”)
receiving scenario input data as input to the machine learning model, wherein the scenario input data comprises conditional data input from a user ([0075] “In some examples, the temperature estimation process includes estimating a temperature of housing 19 based on an algorithm that incorporates temperature measurements obtained from one or more temperature sensor(s) that are disposed within IMD 14. In such examples, the algorithm represents a temperature difference between at least two separate portions of IMD 14 based on a dynamic transfer function. The two portions may include a first portion located within a predetermined range of one or more temperature sensors disposed within IMD 14 and a second portion that, in some examples, corresponds to an external surface of housing 19.”) and/or input data obtained from a separately simulated machine learning data change model, the conditional data input including at least one feature dimension associated with at least one of the internal data or the external data, and the input data including at least one feature dimension associated with at least one of the internal data or the external data; and ([0116] “The thermodynamics information may be learned over time based on data obtained via IMD 14 and/or external charging device 22, such as by using a machine learning (ML) or artificial intelligence (AI) algorithm to train IMD 14 or external charging device 22 to better understand the thermodynamics of system 10.”)
generating predicted output based on the scenario input data. ([0075] “In some examples, the temperature estimation process includes estimating a temperature of housing 19 based on an algorithm that incorporates temperature measurements obtained from one or more temperature sensor(s) that are disposed within IMD 14. In such examples, the algorithm represents a temperature difference between at least two separate portions of IMD 14 based on a dynamic transfer function. The two portions may include a first portion located within a predetermined range of one or more temperature sensors disposed within IMD 14 and a second portion that, in some examples, corresponds to an external surface of housing 19. In some examples, the coefficients involved in the temperature estimation process for IMD 14 may include coefficients corresponding to heat capacity of housing 19 and/or coefficients corresponding to internal components of IMD 14. The coefficients may include filter coefficients for a filter, such as a low-pass filter, configured to filter a temperature signal corresponding to the first portion of IMD 14 to estimate the temperature of the second portion of IMD 14 based on a dynamic transfer function (e.g., a first order transfer function). In some examples, IMD 14 may take into account information from other sensors such as acceleration or position sensors to determine a spatial orientation of IMD 14 to the charger.” Examiner Note: The prior arts teaching of estimating the temperature reads on the claimed “predicted output.”)
Regarding Claim 3: The reference discloses The method of claim 1, wherein the differencing the internal data, the external data, and the output data to create the first differenced data comprises generating the differenced data of the internal data, the external data, and the output data based on time information, the differenced data is difference between data at a current time step and a prior time step. (“[0082] In some cases, external charging device 22 may cycle the driving of the primary coil. For instance, external charging device 22 may drive the coil during a first time period, and may discontinue driving the coil for a second time period following the first time period to control an overall transmission of power and in effect, heat generation/dissipation within and around housing 19.”)
Regarding Claim 5: The reference discloses The method of claim 1, further comprising: differencing the scenario input data using a plurality of baselines; calculating second differenced data using the plurality of baselines based on change points associated with the internal data, the external data, and the output data; and generating the predicted output based on the second differenced data. (“[0132] In some examples, multiple temperature readings by IMD 14 may be averaged or otherwise used to produce a single temperature value that is transmitted to external charging device 22. In an example, IMD 14 may transmit a temperature measurement from a temperature sensor that is reading a higher temperature during the charging process relative to other temperature sensors 39. In addition, the sensed and/or estimated temperature may be sampled and/or transmitted by IMD 14 (and received by external charging device 22) at different rates. Processing circuitry 50 may then use the received temperature information to control charging of power source 18 (e.g., control the charging level used to charge power source 18).”)
Regarding Claim 11: The reference discloses A non-transitory computer readable medium, storing instructions for generating simulations based on data, the instructions comprising:
receiving internal data and external data which affect output data; (“[0036] The system may estimate the temperature of the external portion of the device housing using an algorithm that incorporates the one or more temperature measurements corresponding to the internal portion of the device. In general, the temperature estimation algorithm represents a temperature difference between an internal portion of the device and the external portion of the device based on a predetermined dynamic transfer function that operates in the time-domain. Where the device includes multiple temperature sensors, a unique transfer function may be determined for each temperature sensor of the device, where the transfer function is based on a thermal resistance that may be relatively unique for each temperature sensor of the device.” See also “[0072] In some examples, IMD 14 may include a single temperature sensor. In another example, IMD 14 may include a plurality of temperature sensors. In an example, charging head 26 (e.g., external of patient 12) and/or IMD 14 (e.g., implanted within patient 12) may each include one or more temperature sensors.”)
differencing the internal data, the external data, and the output data to create first differenced data; (“[0036] The system may estimate the temperature of the external portion of the device housing using an algorithm that incorporates the one or more temperature measurements corresponding to the internal portion of the device. In general, the temperature estimation algorithm represents a temperature difference between an internal portion of the device and the external portion of the device based on a predetermined dynamic transfer function that operates in the time-domain. Where the device includes multiple temperature sensors, a unique transfer function may be determined for each temperature sensor of the device, where the transfer function is based on a thermal resistance that may be relatively unique for each temperature sensor of the device.”)
training a machine learning model using the first differenced data; ([0116] “The thermodynamics information may be learned over time based on data obtained via IMD 14 and/or external charging device 22, such as by using a machine learning (ML) or artificial intelligence (AI) algorithm to train IMD 14 or external charging device 22 to better understand the thermodynamics of system 10.”)
receiving scenario input data as input to the machine learning model, wherein the scenario input data comprises conditional data input from a user ([0075] “In some examples, the temperature estimation process includes estimating a temperature of housing 19 based on an algorithm that incorporates temperature measurements obtained from one or more temperature sensor(s) that are disposed within IMD 14. In such examples, the algorithm represents a temperature difference between at least two separate portions of IMD 14 based on a dynamic transfer function. The two portions may include a first portion located within a predetermined range of one or more temperature sensors disposed within IMD 14 and a second portion that, in some examples, corresponds to an external surface of housing 19.”) and/or input data obtained from a separately simulated machine learning data change model, the conditional data input including at least one feature dimension associated with at least one of the internal data or the external data, and the input data including at least one feature dimension associated with at least one of the internal data or the external data; and ([0116] “The thermodynamics information may be learned over time based on data obtained via IMD 14 and/or external charging device 22, such as by using a machine learning (ML) or artificial intelligence (AI) algorithm to train IMD 14 or external charging device 22 to better understand the thermodynamics of system 10.”)
generating predicted output based on the scenario input data. ([0075] “In some examples, the temperature estimation process includes estimating a temperature of housing 19 based on an algorithm that incorporates temperature measurements obtained from one or more temperature sensor(s) that are disposed within IMD 14. In such examples, the algorithm represents a temperature difference between at least two separate portions of IMD 14 based on a dynamic transfer function. The two portions may include a first portion located within a predetermined range of one or more temperature sensors disposed within IMD 14 and a second portion that, in some examples, corresponds to an external surface of housing 19. In some examples, the coefficients involved in the temperature estimation process for IMD 14 may include coefficients corresponding to heat capacity of housing 19 and/or coefficients corresponding to internal components of IMD 14. The coefficients may include filter coefficients for a filter, such as a low-pass filter, configured to filter a temperature signal corresponding to the first portion of IMD 14 to estimate the temperature of the second portion of IMD 14 based on a dynamic transfer function (e.g., a first order transfer function). In some examples, IMD 14 may take into account information from other sensors such as acceleration or position sensors to determine a spatial orientation of IMD 14 to the charger.” Examiner Note: The prior arts teaching of estimating the temperature reads on the claimed “predicted output.”)
Regarding Claim 13: The reference discloses The non-transitory computer readable medium of claim 11, wherein the differencing the internal data, the external data, and the output data to create the first differenced data comprises generating the differenced data of the internal data, the external data, and the output data based on time information, the differenced data is difference between data at a current time step and a prior time step. (See rejection for claim 3)
Regarding Claim 15: The reference discloses The non-transitory computer readable medium of claim 11, further comprising: differencing the scenario input data using a plurality of baselines; calculating second differenced data using the plurality of baselines based on change points associated with the internal data, the external data, and the output data; and generating the predicted output based on the second differenced data. (See rejection for claim 5)
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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 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. Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fried in view of Kavikondala, Akanksha, et al. "Automated retraining of machine learning models." International Journal of Innovative Technology and Exploring Engineering 8.12 (2019): 445-452, hereafter K.
Regarding Claim 4: Fried does not explicitly The method of claim 1, further comprising: detecting changes in relationship between the internal data, the external data, and the output data; and determining whether retraining of a new machine learning model is needed based on the detected changes.
However K teaches The method of claim 1, further comprising: detecting changes in relationship between the internal data, the external data, and the output data; and determining whether retraining of a new machine learning model is needed based on the detected changes. (Page 446, Bottom Left, “Our main objective is to build a model that can retrain itself appropriately. To achieve this, we need to: 1. Design an algorithm that can identify and detect a threshold when the model becomes inaccurate. 2. Automate the algorithm and integrate an upgraded algorithm into our model.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the retraining of a model as per K for the model in Fried since “Retraining is essential in cases where the data and the nature of data changes frequently. Retraining can help the model be up to date and not become obsolete.” (K, Page 451, bottom left)
Regarding Claim 14: The reference discloses The non-transitory computer readable medium of claim 11, further comprising: detecting changes in relationship between the internal data, the external data, and the output data; and determining whether retraining of a new machine learning model is needed based on the detected changes. (See rejection for claim 4)
5. Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fried in view of U.S. Patent No. 20180024578, hereafter Ahuja.
Regarding Claim 9: Fried does not explicitly recite The method of claim 1, wherein the predicted output comprises output information showing correlation between featured time associated with a data center's operation and predicted change in cooling power of the data center.
However Ahuja recites The method of claim 1, wherein the predicted output comprises output information showing correlation between featured time associated with a data center's operation and predicted change in cooling power of the data center. ([0051] “The data center manager 1208 predicts, based on the sensor data and a machine-learning-based algorithm, an expected power usage at the node, sled, rack, and data center level for the near future, such as for the next 15 minutes. The data center manager 1208 may control the cooling unit 1206 to adjust to the anticipated cooling load based on the predicted power usage.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the data center operation aspect of Ahuja with the determinations made in Fried since “The machine-learning-based algorithm can predict a change in future power usage of the data center, and control a cooling unit to compensate before the power usage even begins to change.” (Ahuja, Abstract)
Regarding Claim 19: The reference discloses The non-transitory computer readable medium of claim 11, wherein the predicted output comprises output information showing correlation between featured time associated with a data center's operation and predicted change in cooling power of the data center. (See rejection for claim 9)
6. Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fried in view of U.S. Patent Publication No. 20080288193, hereafter Claassen.
Regarding Claim 10: Fried does not explicitly recite The method of claim 1, wherein: the machine learning model simulates relationships between independent input features and dependent output feature; the independent input features comprise internal room temperature, internal room humidity, external humidity, weather data, server heat, and server power consumption associated with a data center; and the dependent output features comprise cooling power consumption associated with the data center and speed of an AC motor or ventilator in associated with operation of the data center.
However Claassen recites The method of claim 1, wherein: the machine learning model simulates relationships between independent input features and dependent output feature; the independent input features comprise internal room temperature, ([0058] “By way of example only, in a data center, hotspots can be identified as those regions of the data center having temperatures that are at least about five .degree. C. greater than, e.g., between about five .degree. C. and about 20.degree. C., the average room temperature, and the hot spot region can be between about 10 percent and about 40 percent of the total room footprint area.”) internal room humidity, (“[0032] In step 108, physical parameter data are collected from the data center. As will be described in detail below, the physical parameter data can include, but are not limited to, temperature, humidity and air flow data for a variety of positions within the data center. According to an exemplary embodiment, the temperature and humidity data are collected front the data center using mobile measurement technology (MMT) thermal scans of the data center.”) external humidity, ([0030] “It is to be understood however, that the efficiency of a given data center can depend on factors, including, but not limited to, geography, country and weather. Therefore, a particular efficiency value might be considered to be within an acceptable range in one location, but not acceptable in another location.” “[0080] Due to one or more of condensation at the cool heat exchanger coils of the ACU, the existence of human beings in the data center who "sweat" moisture into the room, as well as the ingress of external dry or moist air into the room, the humidity of the data center needs to be controlled, i.e., by dehumidification.”) weather data, ([0030] “It is to be understood however, that the efficiency of a given data center can depend on factors, including, but not limited to, geography, country and weather. Therefore, a particular efficiency value might be considered to be within an acceptable range in one location, but not acceptable in another location.”) server heat, (“[0003] Computer equipment is continually evolving to operate at higher power levels. Increasing power levels pose challenges with regard to thermal management. For example, many data centers now employ individual racks of blade servers that can develop 20,000 watts, or more, worth of heat load. Typically, the servers are air cooled and, in most cases, the data center air conditioning infrastructure is not designed to handle the thermal load.”) and server power consumption associated with a data center; (“[0003] Computer equipment is continually evolving to operate at higher power levels. Increasing power levels pose challenges with regard to thermal management. For example, many data centers now employ individual racks of blade servers that can develop 20,000 watts, or more, worth of heat load. Typically, the servers are air cooled and, in most cases, the data center air conditioning infrastructure is not designed to handle the thermal load.”) and the dependent output features comprise cooling power consumption associated with the data center ([0015] FIG. 7 is a diagram illustrating how best practices impact transport and thermodynamic factors of cooling power consumption in a data center according to an embodiment of the present invention”) and speed of an AC motor or ventilator in associated with operation of the data center. ([0068]) “The blower motor speed for this operating point is 800 revolutions per minute (RPM). Observing FIG. 10, it can seen that, on reducing the blower motor speed from 800 RPM to 600 RPM, the air flow rate reduces by 22 percent while the blower motor power consumption reduces by 50 percent (i.e., as compared to the blower motor at 800 RPM).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the thermal analysis between the inside and outside of a system as in Fried for data center thermal variables as in Claassen in order to provide “An initial assessment is made of the energy efficiency of the data center based on one or more power consumption parameters of the data center. Physical parameter data obtained from one or more positions in the data center are compiled into one or more metrics, if the initial assessment indicates that the data center is energy inefficient. Recommendations are made to increase the energy efficiency of the data center based on one or more of the metrics.” See [0007] of Claassen.
Regarding Claim 20: The reference discloses The non-transitory computer readable medium of claim 11, wherein: the machine learning model simulates relationships between independent input features and dependent output feature; the independent input features comprise internal room temperature, internal room humidity, external humidity, weather data, server heat, and server power consumption associated with a data center; and the dependent output features comprise cooling power consumption associated with the data center and speed of an AC motor or ventilator in associated with operation of the data center. (See rejection for claim 10)
Allowable Subject Matter
7. Claims 2, 6-8, 12, and 16-18 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:
Claim 2 recites: The method of claim 1, further comprising: grouping the internal data based on select granularity, wherein the select granularity is based on available grouping information contained within the internal data; resampling the grouped internal data and the external data based on different sampling frequencies to establish correlation between the internal data, the external data, and the output data; and identifying a time sampling frequency having an optimal correlation distribution among the different sampling frequencies and using the identified time sampling frequency as sampling frequency to prepare the internal data, the external data, and the output data for training the machine learning model.
Claim 6, of which claims 7 and 8 depend, recites: The method of claim 5, further comprising: generating real value returned output through addition of the plurality of baselines to the predicted output, wherein the differencing the scenario input data using the plurality of baselines is performed by subtracting the plurality of baselines from the scenario input data to generate the second differenced data.
Claim 12 recites: The non-transitory computer readable medium of claim 11, further comprising: grouping the internal data based on select granularity, wherein the select granularity is based on available grouping information contained within the internal data; resampling the grouped internal data and the external data based on different sampling frequencies to establish correlation between the internal data, the external data, and the output data; and identifying a time sampling frequency having an optimal correlation distribution among the different sampling frequencies and using the identified time sampling frequency as sampling frequency to prepare the internal data, the external data, and the output data for training the machine learning model.
Claim 16, of which claims 17 and 18 depend, recites: The non-transitory computer readable medium of claim 15, further comprising: generating real value returned output through addition of the plurality of baselines to the predicted output, wherein the differencing the scenario input data using the plurality of baselines is performed by subtracting the plurality of baselines from the scenario input data to generate the second differenced data.
The closest prior art of record includes:
Zhang, Kaicheng, et al. "Minimizing thermal variation across system components." 2015 IEEE International Parallel and Distributed Processing Symposium. IEEE, 2015.
ii) Zhang, Kaicheng, et al. "Machine learning-based temperature prediction for runtime thermal management across system components." IEEE Transactions on parallel and distributed systems 29.2 (2017): 405-419.
iii) U.S. Patent Publication No. 20200389371 which teaches predicting network states in answering what if scenario outcomes.
However, the closest prior art of record does not explicitly teach or render obvious the limitations above,
particularly in combination with the other limitations within the claims. The dependent claims are allowable for at least the same reasons as their respective independent claims.
Conclusion
8. Claims 1, 3-5, 9-11, 13-15, and 19-20 are rejected.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang, Kaicheng, et al. "Minimizing thermal variation across system components." 2015 IEEE International Parallel and Distributed Processing Symposium. IEEE, 2015.
ii) Zhang, Kaicheng, et al. "Machine learning-based temperature prediction for runtime thermal management across system components." IEEE Transactions on parallel and distributed systems 29.2 (2017): 405-419.
iii) U.S. Patent Publication No. 20200389371 which teaches predicting network states in answering what if scenario outcomes.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186