DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-21 are pending in this Application.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 21 is rejected under 35 USC 101 because the claimed invention is directed to non-statutory subject matter.
Claim 21, lines 1-3, recite "A computer-readable storage medium with instructions stored thereon, wherein when the instructions are executed by a processor, the processor is configured to execute the method according to claim 11…”.
The broadest reasonable interpretation of a computer-readable storage medium covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer usable medium (see MPEP 2111.01), wherein the transitory propagating signals are non-statutory subject matter.
Paragraph [0081] of the PgPub recites “Computer-readable storage mediums may include, for example,... any other transitory or non-transitory mediums that can be used to carry or store desired program code elements in the form of instructions or data structures and can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor”. Applicant can overcome this rejection by adding the term “non-transitory” in claim 21.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
The claim(s) 1, 11, and 21 recite(s) in part “obtaining user evaluations of an environment and environmental parameter measurements; and generating evaluation information about the environment from the user evaluations and the environmental parameter measurements”. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers data obtaining/collection, evaluation/comparing and judgment/subjective prediction which are steps that can be easily performed mentally and belong to the group of mental processes abstract idea. For instance, a user based on obtaining or reading environment measurements, and his/her subjective perception of the temperature in the environment can easily predict that the environment is too crowded and/or the quality of the air is low or not comfortable. Claims 1 and 21 further recite a processor and memory for performing the steps above. That is other, than reciting a processor and its memory, nothing in the claim precludes these steps from practically being performed in the human mind. However, the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer (see 2106.04III). Thus, the claims recites a mental process.
This judicial exception is not integrated into a practical application because the additional elements such as a processor and memory and which seem to be computer components such as a computer, are recited in higher level of generality and simply correspond to merely reciting the words “apply it” with the judicial exception to a computer (see 2106.04(d)(1), and 2106.05(f)). The claims recite that the measurements and user evaluations are obtained from a plurality of mobile devices, which are recited in a high level of generality and considered insignificant extra solution and pre-solution activities of mere data gathering (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the inventions are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer and memory to perform the steps of the method which are recited in high level of generality and simply represents no more than instructions “to apply” with the judicial exception/the abstract idea on a computer or to generally link the use of the judicial exception to the technological environment of a computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(f) and MPEP 2106.05(h)). Furthermore, the step of obtaining the data from mobile devices are mere data gathering steps that have been considered insignificant extra solution that does not amount to significantly more as indicated by the courts since they insignificant extra solution activities (see MPEP 2105.05 (g) See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)). Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 2-10, 12-20 depends on claim 1 and 11 respectively. and thus recites the limitations and the abstract ideas of claim 1 and 11.
Claims 2-3 and 12-13 further recites “wherein the mobile device comprises at least one of: a terminal device with a human-machine interface, a terminal device with one or more built-in sensors, and a terminal device with a human-machine interface and one or more built-in sensors” and “wherein the user evaluations are in the form of natural language or in a prescribed data format” which are recited at a high level of generality and simply represents well-understood, routine, conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements such mobile device comprises at least one of: a terminal device with a human-machine interface, a terminal device with one or more built-in sensors, and a terminal device with a human-machine interface and one or more built-in sensors”, “wherein the user evaluations are in the form of natural language or in a prescribed data format” and simply involve appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). For instance, Cui et al (US 20190158305), teaches a terminal device with a human-machine interface and one or more built-in sensors (se Fig. 1-2 mobile device are terminal devices with sensors 152 and HMI to input user data; see 0021), and wherein the user evaluations are in the form of natural language or in a prescribed data format (0039). Also, the references Cooper (US 20200229514 cited in IDs, see ), Hillaire et al (US 20140113267 see 0043), Combs (US 20190373426, see 0058) teaches the limitations of claim 2-3 and 12-13, . Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claim 4, 10, 14, and 20 further recites “wherein the environmental parameter measurements include one or more of: temperature, humidity, illuminance, sound intensity, substance concentration, and particle concentration”, and “wherein the evaluation information is one or more of: current air quality level, predicted value of air quality level, estimate value of space crowdedness and correlation between the user evaluations and the environmental parameter measurements”, recited at high level of generality represents an insignificant extra solution activity of mere data gathering and data outputting selecting a particular type of data such as temperature, humidity, illuminance, sound intensity, substance concentration, and particle concentration (2106.05(g)) or current air quality level, predicted value of air quality level, estimate value of space crowdedness and correlation between the user evaluations and the environmental parameter measurements. Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 5 and 15 further recite “wherein the user evaluations and the environmental parameter measurements have at least one of a location tag and a time tag”, recited at high level of generality simply represents well-understood, routine, conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements such as wherein the user evaluations and the environmental parameter measurements have at least one of a location tag and a time tag simply involve appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). For instance, Cui et al (US 20190158305), teaches a wherein the user evaluations and the environmental parameter measurements have at least one of a location tag and a time tag (see [0029]). Orfield (US 20060184325, see claim 42), Wang et al (US 20120027544, 0030, 0033), Rayner et al (US 20140031703, see 0045), Hilaire et al (US 20140113267) teaches tagging transmitted information with location of the collected data. Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 6 and 16 further recite “wherein the evaluation information is generated using a machine learning model, and wherein the machine learning model is trained using historical data of the user evaluations and the environmental parameter measurements”, recited at high level of generality simply represents no more than mere instructions to apply the judicial exception on a computer using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 7 and 17 further recite “intercalibrating sensors of the mobile devices using a plurality of the environmental parameter measurements”, recited at high level of generally and represent tangential limitations that do not integrate the Abstract ideas of claims 1 and 11. For instance, intercalibrating the sensors is not tied to the abstract idea described above and does not does not impose any meaningful limits on practicing the abstract idea of claims 1 and 11. Furthermore, the limitations “intercalibrating sensors of the mobile devices using a plurality of the environmental parameter measurements” recited at high level of generality simply represents well-understood, routine, conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Accordingly, these additional elements do not integrate the abstract idea into a practical application. For instance, Edge et al (US 20140135040) teaches the intercalibration of sensors in mobile devices (see 0040-0047), Ahuja et a (US 9534924, see Col 2), Dormody et al (US 20200045668, see 0046), Florentino (US 20170300045, see 0002, 0009) teaches the intercalibration of sensors in mobile devices. Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 8-9 and 19-20 further recites “executing a cross-check based on the user evaluations and the environmental parameter measurements”, and “executing a data spoofing detection based on the location tag contained in the user evaluations and the environmental parameter measurements” which are recited at high level of generality and represent tangential limitations that do not integrate the Abstract ideas of claims 1 and 11 and does not does not impose any meaningful limits on practicing the abstract idea of claims 1 and 11. Furthermore, the limitations “cross checking data” and “data spoofing detection” recited at high level of generality simply represents well-understood, routine, conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Accordingly, these additional elements do not integrate the abstract idea into a practical application. For instance, Endel et al (20180202677) teaches cross checking od data (see 0038-0041); Korrapati et al (US 20200068408, 0020-0024), Maliya et al (US 20230007488) and/or Lumezanu et al (2019009849) teaches data spoofing detection. Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
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.
Claim(s) 1-5, 11-15 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cui et al (US 20190158305).
As per claim 1, Cui teaches a data processing system (see Fig. 1 system 100 including server 102), comprising:
at least one processor (see Fig. 1 processor 120, see 0019);
at least one memory (see Fig. 1 memory 124);
and a computer program stored on the memory and executable on the processor (see [0018] and see [0036] “…, in use, the building automation application server 102 may execute a method 300 for individualized building automation…”), wherein execution of the computer program on the processor results in the following operations:
obtaining user evaluations of an environment (see [0039] “… in block 310 the building automation application server 102 may receive individualized sensor data that includes or was generated from user input data received by the mobile computing device 104…In some embodiments, individualized sensor data may be indicative of a user sentiment expressed by the user in the user input. For example, the user may input text or images regarding the user's current comfort level (e.g., text indicating the current temperature). The building automation application server 102 and/or the mobile computing device 104 may parse or otherwise analyze the user input to determine user sentiment…”; also, see [0051] “in block 430 the mobile computing device 104 may monitor user input for user sentiment that indicates the user's comfort level. For example, the user may input text that indicates the user's current comfort level (e.g., entering a text message stating, “It's too cold in here”). The mobile computing device 104 may parse or otherwise analyze the user input to determine the user sentiment, or the mobile computing device 104 may transmit the user input to the building automation application server 102 for analysis”) and environmental parameter measurements from a plurality of mobile devices (see [0039] “…in block 308 the building automation application server 102 may receive individualized sensor data that includes or was generated from sensor data collected by the mobile computing device 104. For example, the building automation application server 102 may receive sensor data indicative of the environment of the mobile computing device 104, such as audio data, visual/camera data, temperature data, humidity data, light level data, or other environmental data…”; also, see [0023] “the sensors 152 may include sensors capable of measuring the environment of the mobile computing device 104, including sensors capable of measuring temperature, humidity, light levels, or other environmental sensors. ..”); and
generating evaluation information about the environment from the user evaluations and the environmental parameter measurements (see [0041] [0042] “s, in block 316 the building automation application server 102 may model or otherwise measure the environment of the building 110 based on the aggregate individualized sensor data received from the mobile computing devices 104 and/or the fixed sensor data received from the fixed sensors. For example, the building automation application server 102 may use distributed majority algorithms to accurately determine the temperature, humidity, light level, or other environmental factors for the building 110 or for parts of the building 110. As another example, the building automation application server 102 may determine whether particular parts of the building 110 are occupied based on the individualized sensor data….”; also, see [0043] and see Fig. 3 and block 314 which comprises evaluation information based on the received data).
As per claim 2, Cui teaches the data processing system according to claim 1, Cui further teaches wherein the mobile device comprises at least one of:
a terminal device with a human-machine interface (see Fig. 2 mobile device 104 and user input module 226), a terminal device with one or more built-in sensors (see Fig. 1 sensors 152), and a terminal device with a human-machine interface and one or more built-in sensors (se Fig. 1-2 mobile device are terminal devices with sensors 152 and HMI to input user data; see Fig. 102 interface module to receive users input and sensor input ; [0021] “Illustratively, the mobile computing device 104 includes…components and devices commonly found in a smart phone or similar computing device (e.g., a touchscreen display or other I/O devices).
As per claim 3, Cui teaches the data processing system according to claim 1, Cui further teaches wherein the user evaluations are in the form of natural language or in a prescribed data format (see [0039] “…or example, the user may input text that indicates the user's current comfort level (e.g., entering a text message stating, “It's too cold in here…”).
As per claim 4, Cui teaches the data processing system according to claim 1, Cui further teaches wherein the environmental parameter measurements include one or more of: temperature, humidity, illuminance, sound intensity, substance concentration, and particle concentration (see [0023] “the sensors 152 may include sensors capable of measuring the environment of the mobile computing device 104, including sensors capable of measuring temperature, humidity, light levels, or other environmental sensors. ..”).
As per claim 5, Cui teaches the data processing system according to claim 1, Cui further teaches wherein the user evaluations and the environmental parameter measurements have at least one of a location tag and a time tag (see [0029] “The individualized sensor data is indicative of the location of each mobile computing device 104 and a building system control parameter measured or otherwise determined by the mobile computing device 104”; also, see [0037 “In block 304, the building automation application server 102 receives geo- and time-tagged individualized sensor data from one or more of the mobile computing devices 104. The individualized sensor data is geo-tagged, meaning that it includes or is otherwise associated with geographical data indicating the location of the associated mobile computing device 104….”).
As per claim 11,Cui teaches a method for environment evaluation, comprising the steps of (see [0041-0043] and see Figs. 3-4 ):
obtaining user evaluations of an environment (see [0039], [0051],) and environmental parameter measurements from a plurality of mobile devices ([0023] and [0039]); and
generating evaluation information about the environment from the user evaluations and the environmental parameter measurements (see [0041]-[0043], also, see claim 1 above same rationale is incorporated by reference herein).
As to claim 12, this claim is the method claim corresponding to the system claim 2 and is rejected for the same reasons mutatis mutandis.
As to claim 13, this claim is the method claim corresponding to the system claim 3 and is rejected for the same reasons mutatis mutandis.
As to claim 14, this claim is the method claim corresponding to the system claim 4 and is rejected for the same reasons mutatis mutandis.
As to claim 15, this claim is the method claim corresponding to the system claim 5 and is rejected for the same reasons mutatis mutandis.
As per claim 21, Cui teaches computer-readable storage medium with instructions stored thereon (see Fig. 1 memory 124; also, see [0018] and [0036] “…, in use, the building automation application server 102 may execute a method 300 for individualized building automation…”), wherein when the instructions are executed by a processor, the processor is configured to execute the method according to claim 11 (see claim 11 above).
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.
Claim(s) 6, 10, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cui et al (US 20190158305) as applied to claim 1 above, and further in view of Fan et al (US 20190187634).
As per claim 6, Cui teaches the data processing system according to claim 1, Cui does not explicitly teach wherein the evaluation information is generated using a machine learning model, and wherein the machine learning model is trained using historical data of the user evaluations and the environmental parameter measurements.
Fan teaches an environment evaluation control system comprising generating an evaluation information about an environment using a machine learning model (see Fig. 2A-2c user input used in the ML model, see Fig. 3A input data comprises user evaluation 138 and sensor data; also, see [0016] “…The control system includes processing capability 152, which includes a machine learning model 153, and a controller 159. As used herein, the term “machine learning model” is meant to include just a single machine learning model or also an ensemble of machine learning models. Each model in the ensemble may be trained to infer different attributes. The data interface 151 receives various input data, which are processed 152 at least in part by the machine learning model 153…”; also, see [0027], [0034] identify patters such as occupancy/crowdedness and occupant comfort; also, see [0038] “the machine learning model learns to predict the environment in rooms (e.g., temperature, humidity, lighting) and the energy consumption/cost based on historical data”), and wherein the machine learning model is trained using historical data of a user evaluations and environmental parameter measurements ([0038] “the machine learning model learns to predict the environment in rooms (e.g., temperature, humidity, lighting) and the energy consumption/cost based on historical data”; also, see [0040] “The training module receives 511 a training set for training the machine learning model in a supervised manner. Training sets typically are historical data sets of inputs and corresponding responses. The training set samples the operation of the environmental system, preferably under a wide range of different conditions. FIG. 3A gives some examples of input data 310 that may be used for a training set”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cui’s invention to include generating an evaluation information about an environment using a machine learning model, wherein the machine learning model is trained using historical data of the user evaluations and the environmental parameter measurements as taught by Fan in order to predict conditions of an environment (see [0028]) and because the use of machine learning models is beneficial for situations where the precited attribute is complex function of many factors/inputs (see [0028]).
As per claim 10, Cui teaches the data processing system according to claim 1, while Cui taches relating user evaluations and the environmental parameter measurements, Cui does not explicitly teach wherein the evaluation information is one or more of: current air quality level, predicted value of air quality level, estimate value of space crowdedness and correlation between the user evaluations and the environmental parameter measurements.
Fan teaches an environment evaluation control system comprising generating an evaluation information about an environment based on ser evaluations and the environmental parameter measurements (see Fig. 2A-2c user input used in the ML model, see Fig. 3A input data comprises user evaluation 138 and sensor data; also, see [0016] “…The control system includes processing capability 152, which includes a machine learning model 153, and a controller 159. As used herein, the term “machine learning model” is meant to include just a single machine learning model or also an ensemble of machine learning models. Each model in the ensemble may be trained to infer different attributes. The data interface 151 receives various input data, which are processed 152 at least in part by the machine learning model 153…”; also, see [0027], [0034] identify patters such as occupancy/crowdedness and occupant comfort; also, see [0038] “the machine learning model learns to predict the environment in rooms (e.g., temperature, humidity, lighting) and the energy consumption/cost based on historical data”), wherein the evaluation information is one or more of: current air quality level, predicted value of air quality level, estimate value of space crowdedness and correlation between the user evaluations and the environmental parameter measurements (see Fig. 3A occupancy patterns/ Crowdedness or occupant density/crowdedness; also, see [0038] “the machine learning model learns to predict the environment in rooms (e.g., temperature, humidity, lighting) and the energy consumption/cost based on historical data”; also, see [0059] and [0079] “…For example, if the comfort zone is defined as being within a range of temperatures and humidity, then a policy that results in actual temperatures outside the comfort zone for too long when occupants are present is scored poorly. A policy that results in a high volume of occupant complaints is scored poorly…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cui’s invention to include generating an evaluation information about an environment based on ser evaluations and the environmental parameter measurements, wherein the evaluation information is one or more of: current air quality level, predicted value of air quality level, estimate value of space crowdedness and correlation between the user evaluations and the environmental parameter measurements as taught by Fan in order to monitor attribute/metrics/comfort of environments and control a system to reach an optimized attribute/metrics/comfort of the environment (see Fig. 3B and see [0079-[0082]).
As to claim 16, this claim is the method claim corresponding to the system claim 6 and is rejected for the same reasons mutatis mutandis.
As to claim 20, this claim is the method claim corresponding to the system claim 10 and is rejected for the same reasons mutatis mutandis.
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cui et al (US 20190158305) as applied to claim 1 above, and further in view of Edge et al (US 20140135040).
As per claim 7, Cui teaches the data processing system according to claim 1, Cui does not explicitly teach wherein execution of the computer program on the processor further results in the following operation: intercalibrating sensors of the mobile devices using a plurality of the environmental parameter measurements.
Edge teaches a system and method comprising intercalibrating sensors of a mobile devices using a plurality of environmental parameter measurements (see [0040] In certain example implementations, an environment report 106 may be indicative of a local atmospheric pressure and/or an atmospheric pressure history (e.g., barometric pressure(s) measured in hectoPascal (hPa), etc.), a local atmospheric temperature and/or atmospheric temperature history (e.g., ambient temperature measured in centigrade, etc.), a local atmospheric humidity and/or humidity history”; also, [0042] “environment report 106 may be indicative of one or more calibration parameters that may have been applied to or may be applicable to one or more sensor-based measurements used to generate environment report 106. Here, one or more of such calibration parameters may be determined based, at least in part, on information in a reference data report 114 obtained from an electronic device 110.”; see [0043] “environment report 106 may be indicative of whether mobile device 102 is or was more likely located within an indoor environment or an outdoor environment wherein certain sensor-based measurements and/or indoor user-supplied observations were obtained. For example, if mobile device 102 is moving (intermittently or continuously) and stores a history of sensor measurements made at different times and for different locations in environment report 106,…”; also, see [0047] “arrangement 100 may provide for a monitoring capability in which a plurality of mobile devices may be invited, e.g. as part of a crowd-sourcing function, to gather certain data regarding phenomena within a particular environment 108. By way of example, arrangement 100 may provide for a weather monitoring capability wherein a plurality of mobile devices 103 may gather sensor measurements,… generate corresponding environment reports, and transmit such environment reports to electronic device 110”; also, see [0060] “…In some implementations (e.g. when more than one environmental sensor needs to be calibrated), mobile device 102 may repeat block 216 several times at different locations and times (and possibly using different reference data reports 114 for each repetition of block 216) in order to obtain sufficient calibration parameters to accurately adjust the environmental sensor or sensors…”; also, see 0088; also, see Fig. 2 the same steps are applied to the calibration of any of the environment sensors in the mobile device including temperature and humidity sensors).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cui’s invention to include intercalibrating sensors of a mobile devices using a plurality of environmental parameter measurements as taught by Edge in order to obtain more accurate and reliable measurements (see 0088).
As to claim 17, this claim is the method claim corresponding to the system claim 7 and is rejected for the same reasons mutatis mutandis.
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cui et al (US 20190158305) as applied to claim 1 above, and further in view of Endel et al (US 20180202677).
As per claim 8, Cui teaches the data processing system according to claim 5, Cui does not explicitly teach wherein execution of the computer program on the processor further results in in the following operation: executing a cross-check based on the user evaluations and the environmental parameter measurements (the term cross-checking was not defined and the steps to perform this function were not explicitly defined. The disclosure seems to suggests a comparison of the sensor measurements and the user’s evaluations. Thus, the term cross-checking will be interpreted in the broadest reasonable interpretation as suggested in the disclosure as simply a comparison between values).
Endel teaches a system and method for a building comfort control comprising computer program instructions: executing a cross-check based on the user evaluations and the environmental parameter measurements (see [0038-0041] user evaluations/votes are cross checked with measurements and a model to identify deviations; also, see [0063] “ anomalies of the area can be determined based on feedback provided by the plurality of users. For example, a user may be comfortable at 25 degrees Celsius based on feedback provided by the user. In this example, the user may provide feedback that they are not comfortable at 25 degrees Celsius in a particular area when the user has provided feedback that they are comfortable at 25 degrees Celsius in other areas. In this example, a notification can be sent to further inspect the particular area with a notification that the particular area should be inspected for anomalies”, thus, two users may state different evaluations at the same parameter measurements of temperature, which indicates an anomaly; also, see [0064] “some examples, identifying anomalies associated with the area includes comparing real time messages from the plurality of users to historic messages from the plurality of users. In some examples, identifying anomalies for the area includes determining when a real time message is outside a threshold value calculated by the historic messages”; also, see [0065] “For example, a thermostat may be non-functional in the particular area and the actual temperature of the particular area is not the same as the temperature determined by the thermostat. In some examples, the anomalies may not be directly associated with to the air temperature of the area. For example, the air temperature of the particular area may be 25 degrees Celsius, but the quantity of natural light, paint color of the particular area, artwork within the particular area, and/or other features may make the user have an perception of being uncomfortable”, thus, in this case the measurement and the user’s perception are cross-checked and they differ from each other).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cui’s invention to include executing a cross-check based on the user evaluations and the environmental parameter measurements as taught by Endel in order to calculate deviations and anomalies in certain environment areas (see [0041])
As to claim 18, this claim is the method claim corresponding to the system claim 8 and is rejected for the same reasons mutatis mutandis.
Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cui et al (US 20190158305) as applied to claim 1 above, and further in view of Korrapati et al (US 20200068408).
As per claim 9, Reference teaches the data processing system according to claim 5, wherein execution of the computer program on the processor further results in in the following operation: executing a data spoofing detection based on the location tag contained in the user evaluations and the environmental parameter measurements.
Korrapati teaches a system and method comprising computer program instructions: executing a data spoofing detection based on the location tag contained in data transmitted and/or parameter measurements (see Fig. 1a and 8 also see [0020-0024] “Exemplary embodiments described herein utilize at least two different sources of location data for a given IoT device to detect location spoofing of the IoT device… Upon receipt of the primary IoT device location information/data (e.g., from report 110), and the secondary IoT device location information/data (e.g., from wireless location determining function 125), spoofing detection function 120 compares the primary location information with the secondary location information to determine if the absolute value of the distance between them is greater than a certain threshold. If the absolute value is greater than the threshold, spoofing detection function 120 may indicate that the primary location information is erroneous and spoofed…”; see [0045] sensors; also, see [0061]-[0062] the sensors are movable; also, see claim 17 on page 12 comprising computer instructions stored in a memory).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cui’s invention to include computer program instructions: executing a data spoofing detection based on the location tag contained in data transmitted and/or parameter measurements as taught by Korrapati and apply this configuration to the transmission of user evaluations and the environmental parameter measurements with location of Cui in order to detect spoofing in the transmission of data and generate an alarm or notification when spoofing has been detected (see [0020] and [0062]).
As to claim 19, this claim is the method claim corresponding to the system claim 9 and is rejected for the same reasons mutatis mutandis.
Conclusion
The prior art made of record and not relied upon, as cited in PTO form 892, is considered pertinent to applicant's disclosure.
Chen et al (US 20160161137) teaches a system for monitoring an environment comprising receiving user feedback and environment measurements (see Fig. 8) and performing evaluations of the environment based on the received data (see Abstract, 011 d for controlling the comfort degree, which can records a user-feedback and current environment parameters to build a learning model; also, see 0016).
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. Applicant must also show how the amendments avoid or differentiate from such references or objections. See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLVIN LOPEZ ALVAREZ whose telephone number is (571) 270-7686 and fax (571) 270-8686. The examiner can normally be reached Monday thru Friday from 9:00 A.M. to 6:00 P.M.
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/O. L./
Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117