DETAILED ACTION
This communication responsive to the Application No. 19/210,061 filed on May 16, 2025. Claims 1-14 are pending and are directed towards CONTROL METHOD, RECORDING MEDIUM, AND ANOMALOUS DATA SENSING SYSTEM
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/16/2025 and 09/05/2025 were Acknowledge. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 1 and 13 objected to because of the following informalities: duplicated limitation “verifying, by the server, whether the first data is anomalous by comparing information related to a sensing result obtained by the sensing server with the second data”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 4-7 and 9-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang et al. US 2019/0138716 A1 (hereinafter “Huang”)
As per Claims 1, 13 and 14, Huang teaches a control method for an anomalous data sensing system including a server, the control method comprising (detect errors and/or attacks by using network or group attestation across the participating Internet of Things (IoT) sensors. Such attestation (1) leverages relevant context collected from the endpoint(s); and (2) cross-validates data across the readings from various sensors. Huang, para [0012])( the use of public ledger system and “group” attestation to autonomously identify anomalies in an IoT network built for a specific task. A public/private ledger (e.g. Blockchain, etc.) logs data from all of the participating IoT endpoints. Huang, para [0014]), the control method comprising:
obtaining, by the server, first data from a sensing server that senses whether data utilized for a service is anomalous (an attestation entity that is part of the IoT network receives IoT endpoint device data from sensors. Conditions pertaining to the IoT endpoint devices are analyzed with the analysis being based on a set of network policy data. In one embodiment, data from the IoT endpoints is cross validated at step 340. The process determines whether a data anomaly is detected in the cross validation. Huang, para [0034]);
obtaining, by the server, second data from a second device different from a first device that has generated the first data (receives IoT endpoint device data from sensors …an IoT group might be related to fuel consumption with one IoT endpoint being a fuel gauge sensor, another IoT endpoint being an odometer sensor, and perhaps other IoT endpoints being environmental sensors that provide data pertaining to the ambient conditions in which the automobile is operated. Each IoT group can include one or more group operational contexts that define the operational contexts of the group, such as temperature extremes, moisture level extremes, and the like. Huang, para [0034-0036]);
verifying, by the server, whether the first data is anomalous by comparing information related to a sensing result obtained by the sensing server with the second data (the process compares the individual IoT endpoint data to their respective IoT endpoint conditions as defined in group attestation data 405. The process determines as to whether any anomalies were found in one or more of the individual IoT endpoints (decision 565). If any anomalies were found, then decision 565 branches to the “yes” branch to handle the anomaly using predefined process 580 (see FIG. 7 and corresponding text for further processing details). On the other hand, if no anomalies were found in any of the individual IoT endpoints, then decision 565 branches to the “no” branch whereupon, at predefined process 570, the process performs the cross-validation of endpoints routine (see FIG. 6 and corresponding text for further processing details). Based on the results of predefined process 570, the process determines whether any anomalies were found in any of the cross-validations between IoT endpoints (decision 575). If any anomalies were found in the cross-validations, then decision 575 branches to the “yes” branch to handle the anomaly using predefined process 580 (see FIG. 7 and corresponding text for further processing details). However, if no anomalies were found in the cross-validation checking, then decision 575 branches to the “no” branch for further processing. Huang, para [0047-0048]); and
outputting from the server a result of the verifying (If any anomalies were found in the cross-validations, then decision 575 branches to the “yes” branch to handle the anomaly using predefined process 580 (see FIG. 7 and corresponding text for further processing details). However, if no anomalies were found in the cross-validation checking, then decision 575 branches to the “no” branch for further processing. Huang, para [0048]).
As per claim 4, Huang teaches the control method according to claim 1, wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when (i) the first data is pedometer data and the second data is information related to weather (another IoT endpoint being an odometer sensor, and perhaps other IoT endpoints being environmental sensors that provide data pertaining to the ambient conditions in which the automobile is operated Each IoT group can include one or more group operational contexts that define the operational contexts of the group, such as temperature extremes, moisture level extremes, and the like. Huang, para [0036]) and (ii) there is a difference between the pedometer data and the information related to the weather (Each IoT group can also include sets of cross-validation thresholds. Huang, para [0037])( These operational contexts may include such things as maximum and minimum temperatures, maximum and minimum moisture levels, or any other environmental or non-environmental contexts within which the IoT group operates. Huang, para [0038]).
As per claim 5, Huang teaches the control method according to claim 4, wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when there is an increase in a total number of steps in the pedometer data even though the second data indicates bad weather (the speedometer is reporting a speed of 65 miles per hour, the electronic gyroscope is reporting that the vehicle is traveling on a primarily flat surface (i.e., not coasting downhill), and the fuel consumption sensor is reporting that the vehicle is consuming a negligible amount of fuel. While each of these sensor readings are appropriate for the individual sensors (e.g., a 65 MPH reading from a speedometer is within acceptable range of the speedometer, while a 650 MPH reading would be deemed a sensor error, a flat reading from the gyroscope is an acceptable reading from the gyroscope, and a negligible fuel consumption is an acceptable reading from the fuel consumption sensor when the vehicle is idling or coasting), the combined set of sensor readings is not acceptable. When this particular vehicle is traveling at 65 MPH on a flat surface a particular amount of fuel consumption is expected with that amount being more than a negligible amount. So, in this case, either the speedometer sensor is defective with the vehicle actually traveling 0 MPH (idling), the gyroscope is defective with the vehicle actually traveling downhill (coasting), or the fuel consumption sensor is defective with the vehicle actually consuming more than a negligible amount of fuel. Moreover, multiple sensors might be defective (e.g., both the gyroscope and the fuel consumption sensor, etc.). By using group attestation, the system cross-validates the readings from the individual sensors and determines that one or more of the readings are not appropriate given the readings received from the other sensors. An error is then detected due to the cross-validation error and the system reports the error to an error log. Huang, para [0016]).
As per claim 6, Huang teaches the control method according to claim 1, wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when pedometer data measured is obtained from the first device as the first data, and a total number of steps measured in a predetermined period of time in the pedometer data is greater than or equal to a threshold value (Each IoT group can also include sets of cross-validation thresholds. For example, a set of cross-validation thresholds might include a fuel usage sensor, a speedometer, and an accelerometer that senses whether the automobile is traveling uphill or downhill. When the speed is at a certain value, such as fifty MPH, and the accelerometer senses that the automobile is traveling uphill or on a flat road, if the fuel gauge senses little or no fuel consumption, then a cross-validation condition might be detected indicating a problem with the fuel consumption sensor. Huang, para [0037]).
As per claim 7, Huang teaches the control method according to claim 1, wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when pedometer data including a total number of steps measured and position information indicating a measurement position is obtained from the first device as the first data, and the pedometer data includes a total number of steps measured while the position information remains unchanged for at least a predetermined amount of time (the speedometer is reporting a speed of 65 miles per hour, the electronic gyroscope is reporting that the vehicle is traveling on a primarily flat surface (i.e., not coasting downhill), and the fuel consumption sensor is reporting that the vehicle is consuming a negligible amount of fuel. While each of these sensor readings are appropriate for the individual sensors (e.g., a 65 MPH reading from a speedometer is within acceptable range of the speedometer, while a 650 MPH reading would be deemed a sensor error, a flat reading from the gyroscope is an acceptable reading from the gyroscope, and a negligible fuel consumption is an acceptable reading from the fuel consumption sensor when the vehicle is idling or coasting), the combined set of sensor readings is not acceptable. When this particular vehicle is traveling at 65 MPH on a flat surface a particular amount of fuel consumption is expected with that amount being more than a negligible amount. So, in this case, either the speedometer sensor is defective with the vehicle actually traveling 0 MPH (idling), the gyroscope is defective with the vehicle actually traveling downhill (coasting), or the fuel consumption sensor is defective with the vehicle actually consuming more than a negligible amount of fuel. Moreover, multiple sensors might be defective (e.g., both the gyroscope and the fuel consumption sensor, etc.). By using group attestation, the system cross-validates the readings from the individual sensors and determines that one or more of the readings are not appropriate given the readings received from the other sensors. An error is then detected due to the cross-validation error and the system reports the error to an error log. Huang, para [0016]).
As per claim 9, Huang teaches the control method according to claim 1, wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when the first data is obtained from the first device and information indicating that the first device is abnormal is obtained (Based on the results of predefined process 570, the process determines whether any anomalies were found in any of the cross-validations between IoT endpoints (decision 575). If any anomalies were found in the cross-validations, then decision 575 branches to the “yes” branch to handle the anomaly using predefined process 580 (see FIG. 7 and corresponding text for further processing details). However, if no anomalies were found in the cross-validation checking, then decision 575 branches to the “no” branch for further processing. Huang, para [0048]).
As per claim 10, Huang teaches the control method according to claim 1, wherein said verifying whether the first data is anomalous further comprises determining, by the server, credibility of the first data and including, into the first data, the credibility determined, when the first data is sensed as not being anomalous (determines as to whether an individual IoT endpoint anomaly was detected (decision 740). If an individual IoT endpoint anomaly was detected, then decision 740 branches to the “yes” branch whereupon, at step 750, the process reports the anomaly found between the expected endpoint conditions and the IoT endpoint's data. For example, the individual IoT endpoint anomaly might be a likely sensor failure, etc. The individual IoT endpoint anomaly data is reported by writing the individual IoT endpoint anomaly to anomaly report data store 730. On the other hand, if an individual IoT endpoint anomaly was not detected, then decision 740 branches to the “no” branch bypassing step 750. Huang, para [0057]).
As per claim 11, Huang teaches the control method according to claim 1, further comprising:
obtaining, by the sensing server, the first data from the first device (an attestation entity that is part of the IoT network receives IoT endpoint device data from sensors. Huang, para [0034]);;
transmitting, by the sensing server, the first data to the server (Conditions pertaining to the IoT endpoint devices are analyzed with the analysis being based on a set of network policy data. In one embodiment, data from the IoT endpoints is cross validated at step 340. The process determines whether a data anomaly is detected in the cross validation (decision 350). Huang, para [0034]); and
obtaining, by the sensing server, the result of the verifying output from the server (Based on the results of predefined process 570, the process determines whether any anomalies were found in any of the cross-validations between IoT endpoints (decision 575). If any anomalies were found in the cross-validations, then decision 575 branches to the “yes” branch to handle the anomaly using predefined process 580 (see FIG. 7 and corresponding text for further processing details). However, if no anomalies were found in the cross-validation checking, then decision 575 branches to the “no” branch for further processing. Huang, para [0048]).
As per claim 12, Huang teaches the control method according to claim 1, wherein the second data is data related to an area within a predetermined range from an area of the first device (external information, such as device's location, relevant world events, and the like, are also considered as part of the context data for detecting anomalies …the external sources are selected from a group consisting of a device sensor, a GPS that provides a geographic location, and an environmental sensor that provides a set of environmental data. Huang, para [0014]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. US 2019/0138716 A1 (hereinafter “Huang”) in view of Wang et al. CN106570593 A published on 04/19/2017 (hereinafter “Wang”)
As per claim 2, Huang teaches the control method according to claim 1. Huang does not explicitly teach wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when (i) the first data is information related to natural energy and the second data is information related to weather and (ii) there is a difference between the information related to the natural energy and the information related to the weather.
However, Wang teaches wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when (i) the first data is information related to natural energy and the second data is information related to weather (a method for repairing photovoltaic power plant output data based on weather information. Wang Para [0010]) and (ii) there is a difference between the information related to the natural energy and the information related to the weather (examine the correlation between the selected weather information data items and the normal photovoltaic power station output data item by item, and remove irrelevant data items. Wang, Para [0013])(For continuous abnormal data, temperature and cloud cover coefficient data are obtained using weather information at the corresponding time. Photovoltaic power station capacity is obtained by sampling the photovoltaic cell status at the beginning and end of the data. Based on temperature and cloud cover coefficient data and operating capacity data, the output data of the repaired photovoltaic power station is obtained. Wang, Para [0016]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teaching of Huang in view of Wang. One would be motivated to do so, to identify anomalous data at natural energy generators.
As per claim 3, Huang teaches the control method according to claim 2. Huang does not explicitly teach wherein the information related to the natural energy is data of a solar power generation device, and said verifying whether the first data is anomalous includes sensing the first data as being anomalous when the data of the solar power generation device is present even though the second data indicates bad weather.
However, Wang teaches wherein the information related to the natural energy is data of a solar power generation device (a method for repairing photovoltaic power plant output data based on weather information. Wang Para [0010]), and said verifying whether the first data is anomalous includes sensing the first data as being anomalous when the data of the solar power generation device is present even though the second data indicates bad weather (examine the correlation between the selected weather information data items and the normal photovoltaic power station output data item by item, and remove irrelevant data items. Wang, Para [0013])(For continuous abnormal data, temperature and cloud cover coefficient data are obtained using weather information at the corresponding time. Photovoltaic power station capacity is obtained by sampling the photovoltaic cell status at the beginning and end of the data. Based on temperature and cloud cover coefficient data and operating capacity data, the output data of the repaired photovoltaic power station is obtained. Wang, Para [0016]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teaching of Huang in view of Wang. One would be motivated to do so, to identify anomalous data at natural energy generators.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. US 2019/0138716 A1 (hereinafter “Huang”) in view of Li CN 114061791 A published on 02/18/2022 (hereinafter “Li”)
As per Claim 8, Huang teaches the control method according to claim 1. Huang does not explicitly teach wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when pedometer data including a total number of steps measured and a heart rate measured during the measurement of the steps is obtained from the first device as the first data, and the pedometer data includes a total number of steps measured while the heart rate remains unchanged for at least a predetermined amount of time.
However, Li teaches wherein said verifying whether the first data is anomalous includes sensing the first data as being anomalous when pedometer data including a total number of steps measured and a heart rate measured during the measurement of the steps is obtained from the first device as the first data, and the pedometer data includes a total number of steps measured while the heart rate remains unchanged for at least a predetermined amount of time. (obtaining the average value corresponding to the heart rate, the sum of the moving step number, and after the average value corresponding to the environment temperature, determining the compensation coefficient level of the heart rate according to the average value corresponding to the heart rate, determining the compensation coefficient level corresponding to the motion step number according to the motion step number, and determining the compensation coefficient level corresponding to the environment temperature according to the average value corresponding to the environment temperature. judging the heart rate, the compensation coefficient level corresponding to the movement step number and the environment temperature is the same compensation coefficient level, if so, determining the obtained data is normal, and determining the compensation coefficient of the actual temperature according to the same compensation coefficient level, so as to compensate the actual body temperature of the current body temperature. if the corresponding compensation coefficient is not the same compensation coefficient, then determining the obtained data is abnormal. Li, para [0066]-[0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Huang in view of Li. One would be motivated to do so, to identify malicious data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. Yamada US 2020/0036745 A1 directed to abnormality detection device.
B. Sridhara et al. US 2015/0101048 A1 directed to malware detection and prevention method.
C. Jin et al. US 2023/0153430 A1 directed to method for detecting anomalous event in network system.
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Respectfully Submitted
/KHALID M ALMAGHAYREH/Primary Examiner, Art Unit 2492
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