DETAILED ACTIONS
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 11/14/2025. 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 Rejections- 35 USC §101
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 therefore, subject to the conditions and requirements of this title.
Claims 1, 8-9, and 17 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The following analysis is based on claims,
Regarding claim 1,
A method comprising:
identifying sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period;
analyzing the sensor data, and determining attributes of location conditions related to the asset;
compiling, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level; and
storing, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database.
The claim limitations underlined above are abstract idea, and the remaining limitations are “additional elements.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “analyzing the sensor data, and determining attributes of location conditions related to the asset”, represents mathematical concept/ mathematical evaluation of data set using machine learning Data Integrated (DI)-based computerized framework see (Specification [0004]),“compiling, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level”; and represents mathematical evaluation and generation and storing the analyzed data in the network server. see (Specification [0004]-[008],[0023] –[0024],[0031]-[0032] and [0060]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, encompasses a machine learning Farmwork analysis and simulation of climate data and step of “storing, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database” merely represents insignificant post-solution
activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements “identifying sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period”; are data gathering steps for the particular technological environment or field of use. Identifying sensor data based on conditions environment, time period represent mere data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect improvement to other technology or technical field, and the claim language has no indication of displaying the result to users or any practical integration of the output result such as specification [0033]. Therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as sensor data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 8-9 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 8-9 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and extra additional element describing the data collection location. Furthermore, claims 8-9, do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 10,
A system comprising:
a processor configured to:
identify sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period;
analyze the sensor data, and determine attributes of location conditions related to the asset;
compile, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level; and store, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database.
The claim limitations underlined above are abstract idea, and the remaining limitations are “additional elements.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “a processor , analyze the sensor data, and determine attributes of location conditions related to the asset;
”, represents mathematical concept/ mathematical evaluation of data set using machine learning Data Integrated (DI)-based computerized framework see (Specification [0004]),“ compile, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level”; and represents mathematical evaluation and generation and storing the analyzed data in the network server. see (Specification [0004]-[008],[0023] –[0024],[0031]-[0032] and [0060]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, encompasses a machine learning Farmwork analysis and simulation of climate data and step of “store, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database” merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 10 recites additional elements
“identify sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period”; are data gathering steps for the particular technological environment or field of use. Identifying sensor data based on conditions environment, time period represent mere data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect improvement to other technology or technical field, and the claim language has no indication of displaying the result to users or any practical integration of the output result such as specification [0033]. Therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as sensor data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Regarding claim 17,
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising:
identifying sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period;
analyzing the sensor data, and determining attributes of location conditions related to the asset;
compiling, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level; and
storing, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database.
The claim limitations underlined above are abstract idea, and the remaining limitations are “additional elements.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “analyzing the sensor data, and determining attributes of location conditions related to the asset”;
”, represents mathematical concept/ mathematical evaluation of data set using machine learning Data Integrated (DI)-based computerized framework see (Specification [0004]),“ compiling, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level;” and represents mathematical evaluation and generation and storing the analyzed data in the network server. see (Specification [0004]-[008],[0023] –[0024],[0031]-[0032] and [0060]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, encompasses a machine learning Farmwork analysis and simulation of climate data and step of “storing, over a network, a layered form of data for the asset based at least on the vertical column data structure in a database.” merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 17 recites additional elements “A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising:” and identifying sensor data collected by a sensor at a location for a time period, the sensor data related to atmospheric conditions proximate to an asset at the location during the time period”; are data gathering steps and means for the particular technological environment or field of use. Using a computer and Identifying sensor data based on conditions environment, time period represent mere data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements considered individually and in combination with the other claim elements do not reflect improvement to other technology or technical field, and the claim language has no indication of displaying the result to users or any practical integration of the output result such as specification [0033]. Therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as sensor data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
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 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.
Claims 1, 5-10,14-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Repelli et al. (US 2012/0035898 A1, hereinafter Repelli, IDS ref.)
Regarding Claim 1, Repelli teaches,
A method comprising: identifying sensor data (Repelli, Figure 20, environmental element sensor 2020, hydrological sensor 2030. [0155]) collected by a sensor at a location for a time period. the sensor data related to atmospheric conditions proximate to an asset at the location during the time period; (Repelli, [0060] FIG. 5 illustrates one embodiment of a method of generating environmental element prediction data for a point of interest. Environmental element grid data is collected in step 510”).
analyzing the sensor data and determining attributes of location conditions related to the asset;(Repelli, Figure 5, [0061] In step 520, environmental element observation data is collected. The observation data for a given observation point is the actual environmental element data measured at that site. Thus, the observation data for a given observation point may comprise a latitude, longitude, altitude, timestamp (i.e., date and time) and any number of environmental elements observed values (e.g., temperature, precipitation, etc.),
compiling, based on the determined attributes of the location conditions (Repelli, Figure 3), a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level (Repelli, Figure 4, Figure 6, [0048] “Layers 410-420 of grid elements 402 become layers 460-470 of grid points 452. The distance between the grid points is referred to as the grid length. The grid length is representative of spatial resolution. Vertical levels determine the vertical resolution of the model. The result of NWP is thus a three-dimensional grid of points 452 each of which is associated with an array of meteorological elements at a specific time or time step. [0064] Referring to FIG. 3, any X, Y grid location has a plurality of grid points in a vertical column because of the multiple layers of the model"); and
storing, over a network. (Repelli, Figure 19, [0143] Memory 1940 permits storage of collected data and provides working memory when processor 1930 is performing the computations required to generate point environmental element prediction data. [0142] FIG. 19 illustrates an EEPD 1910 having a generalized communication interface. Communications interface 1920 supports receiving the data. For bi-directional communications, the communications interface 1920 supports both transmitter and receiver functionality (i.e., a transceiver). Bidirectional support would be required, for example, with a client-server-based EEPD. The communications interface is coupled 1922 as appropriate (e.g., wire, antenna, fiber optic, etc.) to communicate with the source of the environmental element data”) a layered form of data for the asset based at least on the vertical column data structure in a database (Repelli, Figure 4, 0015] FIG. 4 illustrates three-dimensional grid elements of a layered global weather model. [0049] Dimensions measured perpendicular to the surface of the earth are referred to as vertical or layer. The grid element height is WZ and defines the vertical resolution for the model”).
Regarding Claim 5, Repelli teaches the method of claim 1,
Repelli further teaches further comprising: receiving a request for data about an event; (Repelli, Figure 13, step 1342, [0109] The client communicates a request for environmental element prediction data at a specified point of interest to a server in step 1342”).identifying, from the database, event information related to the asset (Repelli, Figure 10,Maximum temperature at a point is an event ) , wherein the event information comprises the layered data stored in the database for asset (Repelli, Figure 4, 6, ; [0047] FIG. 4 illustrates a portion of the atmosphere partitioned into a plurality of layers of brick-shaped grid elements. Each grid element 402 has an associated X, Y, Z co-ordinate. Thus, the troposphere may be subdivided into additional layers 410-420 of grid elements for NWP. The result of NWP can be described as an array of one or more meteorological elements such as temperature, humidity, pressure, etc. for each grid point associated with a grid element”) generating, based on the event information, a renderable simulation that provides a visual depiction of atmospheric conditions in the vertical column above the asset in relation to a time of the event.(Repelli,[ 0165] Referring to FIG. 19, for example, such a control signal might be used with respect to the predictions to select one or more specific icons 1942 from a set of icons 1944 for visual indication of predicted weather conditions. (Referring to FIG. 23, step 2330 need not rely on sensed current conditions when generating a control signal or code representative of predicted weather conditions). EEPD 1910 interprets the predictions to generate a control signal or code for each prediction timeframe. The control signal(s) may then be used by the EEPD or an external device to select icons corresponding to the control signals. I/O interface 1950 is utilized to
display the selected icon 1942 on display 1970 thus providing a viewer with a visual indicator corresponding to the predictions for one or more environmental elements and one or more prediction timeframes”)
Regarding Claim 6, Repelli teaches the method of claim 5,
Repelli further teaches further comprising: causing a rendering of the renderable simulation on a device associated with a user that provided the request (Repelli, Figure 11, display, Figure 19 [0166] In one embodiment, the EEPD determines the appropriate icon to display. In an alternative embodiment, an external process uses the I/O interface 1950 to obtain data received or computed by the EEPD (including the control signals or codes generated in response to at least one of the sensed current conditions or the predictions).The external process then uses
the I/O interface 1950 to display the selected icon(s). Each prediction timeframe may have its own control signal to support iconic representation of a plurality of prediction timeframes simultaneously as illustrated by displayed results
1982”).
Regarding Claim 7, Repelli teaches the method of claim 5,
Repelli further teaches wherein the event corresponds to at least one of an occurrence of an activity and a non-occurrence of an activity. (Repelli, [0163] [0163] The control signals of FIGS. 22 and 23 may be generated as a result of logic applied to the predictions or sensed current conditions. The logic may be implemented, for
example, as a look-up table, decision tree, or any other suitable device or data structure. The control signal may simply be an "on/off' type control. Alternatively, the control signal may provide more sophisticated information such as when,
how long, etc. to perform an activity such as irrigation. The EEPD may generate a different control signal for each prediction timeframe. For example, the EEPD may interpret the predictions and/or the sensed current conditions to provide a
control signal for each prediction timeframe”).
Regarding Claim 8, Repelli teaches the method of claim 1,
Repelli further teaches wherein the database is a blockchain. (Repelli, Figure 4, [0088] Once a sufficient history of predicted and observed data is collected, various
statistical techniques (e.g., linear regression, average, etc.), other mathematical techniques, or even artificially intelligent approaches (e.g., neural networks) may be used on the historical prediction errors to estimate the current prediction
error for each prediction timeframe (e.g., one-day, two-day,).
Regarding Claim 9, Repelli teaches the method of claim 1,
Repelli further teaches, wherein the asset is an operational item at the location, wherein the item provides a faculty for operators at the location. (Repelli, Figure 11, [0060] FIG. 5 illustrates one embodiment of a method of generating environmental element prediction data for a point of interest. Environmental element grid data is collected in step 510. The environmental element grid data has a first
spatial resolution defined by a first grid length. Typical grid lengths are 20 km-120 km. [0166] The external process then uses the I/O interface 1950 to display the selected icon(s).
Regarding Claim 10, Repelli teaches,
A system comprising:
a processor (Repelli, Figure 19, Processor 1930) configured to:
identify sensor data collected by a sensor at a location for a time period, the sensor data (Repelli, Figure 20, environmental element sensor 2020, hydrological sensor 2030. [0061] Thus, the observation data for a given observation point may comprise a latitude, longitude, altitude, timestamp (i.e., date and time) ) related to atmospheric conditions proximate to an asset at the location during the time period;(Repelli, [0060] FIG. 5 illustrates one embodiment of a method of generating environmental element prediction data for a point of interest. Environmental element grid data is collected in step 510”).
analyze the sensor data and determine attributes of location conditions related to the asset; ;(Repelli, Figure 5, [0061] In step 520, environmental element observation data is collected. The observation data for a given observation point is the actual environmental element data measured at that site. Thus, the observation data for a given observation point may comprise a latitude, longitude, altitude, timestamp (i.e., date and time) and any number of environmental elements observed values (e.g., temperature, precipitation, etc.),
compile, based on the determined attributes of the location conditions, a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level (Repelli, Figures 3- 4, Figure 6, [0048] “Layers 410-420 of grid elements 402 become layers 460-470 of grid points 452. The distance between the grid points is referred to as the grid length. The grid length is representative of spatial resolution. Vertical levels determine the vertical resolution of the model. The result of NWP is thus a three-dimensional grid of points 452 each of which is associated with an array of meteorological elements at a specific time or time step. [0064] Referring to FIG. 3, any X, Y grid location has a plurality of grid points in a vertical column because of the multiple layers of the model"); and
store, over a network (Repelli, Figure 19, [0143] Memory 1940 permits storage of collected data and provides working memory when processor 1930 is performing the computations required to generate point environmental element prediction data. [0142] FIG. 19 illustrates an EEPD 1910 having a generalized communication interface. Communications interface 1920 supports receiving the data. For bi-directional communications, the communications interface 1920 supports both transmitter and receiver functionality (i.e., a transceiver). Bidirectional support would be required, for example, with a client-server-based EEPD. The communications interface is coupled 1922 as appropriate (e.g., wire, antenna, fiber optic, etc.) to communicate with the source of the environmental element data”) a layered form of data for the asset based at least on the vertical column data structure in a database (Repelli, Figure 4, 0015] FIG. 4 illustrates three-dimensional grid elements of a layered global weather model. [0049] Dimensions measured perpendicular to the surface of the earth are referred to as vertical or layer. The grid element height is WZ and defines the vertical resolution for the model”).
Regarding Claim 14, Repelli teaches the system of claim 10,
Repelli further teaches wherein the processor is further configured to:
Repelli further teaches further comprising: receiving a request for data about an event; (Repelli, Figure 13, step 1342, [0109] The client communicates a request for environmental element prediction data at a specified point of interest to a server in step 1342”).identifying, from the database, event information related to the asset (Repelli, Figure 10,Maximum temperature at a point is an event ) , wherein the event information comprises the layered data stored in the database for asset (Repelli, Figure 4, 6, ; [0047] FIG. 4 illustrates a portion of the atmosphere partitioned into a plurality of layers of brick-shaped grid elements. Each grid element 402 has an associated X, Y, Z co-ordinate. Thus, the troposphere may be subdivided into additional layers 410-420 of grid elements for NWP. The result of NWP can be described as an array of one or more meteorological elements such as temperature, humidity, pressure, etc. for each grid point associated with a grid element”) generating, based on the event information, a renderable simulation that provides a visual depiction of atmospheric conditions in the vertical column above the asset in relation to a time of the event.(Repelli,[ 0165] Referring to FIG. 19, for example, such a control signal might be used with respect to the predictions to select one or more specific icons 1942 from a set of icons 1944 for visual indication of predicted weather conditions. (Referring to FIG. 23, step 2330 need not rely on sensed current conditions when generating a control signal or code representative of predicted weather conditions). EEPD 1910 interprets the predictions to generate a control signal or code for each prediction timeframe. The control signal(s) may then be used by the EEPD or an external device to select icons corresponding to the control signals. I/O interface 1950 is utilized to
display the selected icon 1942 on display 1970 thus providing a viewer with a visual indicator corresponding to the predictions for one or more environmental elements and one or more prediction timeframes”).
Regarding Claim 15, Repelli teaches the system of claim 14,
Repelli further teaches wherein the processor is further configured to: cause a rendering of the renderable simulation on a device associated with a user that provided the request. Repelli, Figure 11, display, Figure 19 [0166] In one embodiment, the EEPD determines the appropriate icon to display. In an alternative embodiment, an external process uses the I/O interface 1950 to obtain data received or computed by the EEPD (including the control signals or codes generated in response to at least one of the sensed current conditions or the predictions). The external process then uses the I/O interface 1950 to display the selected icon(s). Each prediction timeframe may have its own control signal to support iconic representation of a plurality of prediction timeframes simultaneously as illustrated by displayed results1982”).
Regarding Claim 16, Repelli teaches the system of claim 10,
Repelli further teaches wherein the event corresponds to at least one of an occurrence of an activity and a non-occurrence of an activity. (Repelli, [0163] [0163] The control signals of FIGS. 22 and 23 may be generated as a result of logic applied to the predictions or sensed current conditions. The logic may be implemented, for
example, as a look-up table, decision tree, or any other suitable device or data structure. The control signal may simply be an "on/off' type control. Alternatively, the control signal may provide more sophisticated information such as when,
how long, etc. to perform an activity such as irrigation. The EEPD may generate a different control signal for each prediction timeframe. For example, the EEPD may interpret the predictions and/or the sensed current conditions to provide a
control signal for each prediction timeframe”)
Regarding Claim 17, Repelli teaches,
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising (Repelli, Figure 19):
identifying sensor data (Repelli, Figure 20, environmental element sensor 2020, hydrological sensor 2030. [0155]) collected by a sensor at a location for a time period, (Repelli, [0061]” the observation data for a given observation point may comprise a latitude, longitude, altitude, timestamp (i.e., date and time”) the sensor data related to atmospheric conditions proximate to an asset at the location during the time period; (Repelli, [0060] FIG. 5 illustrates one embodiment of a method of generating environmental element prediction data for a point of interest. Environmental element grid data is collected in step 510”).
analyzing the sensor data, and determining attributes of location conditions related to the asset; ;(Repelli, Figure 5, [0061] In step 520, environmental element observation data is collected. The observation data for a given observation point is the actual environmental element data measured at that site. Thus, the observation data for a given observation point may comprise a latitude, longitude, altitude, timestamp (i.e., date and time) and any number of environmental elements observed values (e.g., temperature, precipitation, etc.),
compiling, based on the determined attributes of the location conditions (Repelli, Figure 3), a vertical column data structure, the vertical column data structure comprising layered atmospheric data extending upwards from a surface level of the asset to a predetermined value above the surface level (Repelli, Figure 4, Figure 6, [0048] “Layers 410-420 of grid elements 402 become layers 460-470 of grid points 452. The distance between the grid points is referred to as the grid length. The grid length is representative of spatial resolution. Vertical levels determine the vertical resolution of the model. The result of NWP is thus a three-dimensional grid of points 452 each of which is associated with an array of meteorological elements at a specific time or time step. [0064] Referring to FIG. 3, any X, Y grid location has a plurality of grid points in a vertical column because of the multiple layers of the model"); and
storing, over a network. (Repelli, Figure 19, [0143] Memory 1940 permits storage of collected data and provides working memory when processor 1930 is performing the computations required to generate point environmental element prediction data. [0142] FIG. 19 illustrates an EEPD 1910 having a generalized communication interface. Communications interface 1920 supports receiving the data. For bi-directional communications, the communications interface 1920 supports both transmitter and receiver functionality (i.e., a transceiver). Bidirectional support would be required, for example, with a client-server-based EEPD. The communications interface is coupled 1922 as appropriate (e.g., wire, antenna, fiber optic, etc.) to communicate with the source of the environmental element data”) a layered form of data for the asset based at least on the vertical column data structure in a database (Repelli, Figure 4, 0015] FIG. 4 illustrates three-dimensional grid elements of a layered global weather model. [0049] Dimensions measured perpendicular to the surface of the earth are referred to as vertical or layer. The grid element height is WZ and defines the vertical resolution for the model”).
Regarding Claim 20, combination of Repelli and Cook teaches the non-transitory computer-readable storage medium of claim 17,
Repelli further teaches further comprising, further comprising: receiving a request for data about an event receiving a request for data about an event; (Repelli, Figure 13, step 1342, [0109] The client communicates a request for environmental element prediction data at a specified point of interest to a server in step 1342”).identifying, from the database, event information related to the asset (Repelli, Figure 10,Maximum temperature at a point is an event ) , wherein the event information comprises the layered data stored in the database for asset (Repelli, Figure 4, 6, ; [0047] FIG. 4 illustrates a portion of the atmosphere partitioned into a plurality of layers of brick-shaped grid elements. Each grid element 402 has an associated X, Y, Z co-ordinate. Thus, the troposphere may be subdivided into additional layers 410-420 of grid elements for NWP. The result of NWP can be described as an array of one or more meteorological elements such as temperature, humidity, pressure, etc. for each grid point associated with a grid element”) generating, based on the event information, a renderable simulation that provides a visual depiction of atmospheric conditions in the vertical column above the asset in relation to a time of the event.(Repelli,[ 0165] Referring to FIG. 19, for example, such a control signal might be used with respect to the predictions to select one or more specific icons 1942 from a set of icons 1944 for visual indication of predicted weather conditions. (Referring to FIG. 23, step 2330 need not rely on sensed current conditions when generating a control signal or code representative of predicted weather conditions). EEPD 1910 interprets the predictions to generate a control signal or code for each prediction timeframe. The control signal(s) may then be used by the EEPD or an external device to select icons corresponding to the control signals. I/O interface 1950 is utilized to
display the selected icon 1942 on display 1970 thus providing a viewer with a visual indicator corresponding to the predictions for one or more environmental elements and one or more prediction timeframes”).
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.
Claims 2-4, 11-13, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Repelli and in view of Cook et al. (US 2024/0272103 A1, hereinafter Cook).
Regarding Claim 2, Repelli teaches the method of claim 1,
Repelli is silent on identifying information related to another sensor for another asset. identifying, based on the information for the other sensor, stored layered data for the asset; analyzing the information for the other sensor based on the stored layered data; and determining a configuration for the other sensor, the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor.
However, Cook teaches identifying information related to another sensor for another asset (Cook, Figures 2J, Step 286A, Identify sensor). identifying, based on the information for the other sensor, stored layered data for the asset;(Cook, Figure 2I, “[0510] As shown, the sensor as a service platform 280 may be connected to the cloud sensor resources 212. In one embodiment, the cloud sensor resources 212 may be used to manage and partition sensors. For example, the sensors (shown as sensors 284Al, 284A2, 284B1, 284B2, 284Nl, and 284N2) may be grouped based on a class (shown as sensor asset class 282A, sensor asset class 282B, and sensor asset class 282N).(…) a sensor asset class may include a grouping of sensors based on properties (such as attributes, etc.) and behaviors (such as permission levels, etc.)”) analyzing the information for the other sensor based on the stored layered data (Cook, Figure 2I, Could sensor resource 212); and determining a configuration for the other sensor, (Cook, Figure 2J, [0522] As such, the method 219 relates to the additional information that may be gleaned from a sensor. Such information may be provided to a sensor database, which in turn, may be used to update signatures at other sensors as well. In this manner, data obtained and analyzed from one sensor may be used to increase the intelligence with other sensors as well (and vice versa”) the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor. (Cook, Figure 2K-2L, [0523] The method 219 may be further enhanced by combining it with artificial intelligence (AI) and/or machine
learning capabilities. For example, per operation 288C, if the detected data is new, an AI system and/or machine learning system may be used to determine and compute what the detected data relates to (a new signature profile, etc.) [0526] Instruction is received to configure capability of one or more sensors. See operation 290C. Additionally, instructions are sent to update the one or more sensors based on the configured capability. In particular, the architecture 213 and architecture 215 may be especially pertinent in relating to updating and configuring sensors”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Regarding Claim 3, combination of Repelli and Cook teaches the method of claim 2,
Repelli is silent on further comprising: configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities.
However, Cook teaches further comprising: configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities (Cook, Figure 7, Figure 8, [0675] The classification management system 810 may be in communication with new signature analysis 812 when it is determined by the classification management system 810 that a new signature is received from the sensor data 802. The new signature analysis 812 then communicates the data to relevancy to signatures 814 which is then assigned a confidence 816”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512], [0673]-[0677]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Regarding Claim 4, combination of Repelli and Cook teaches the method of claim 3,
Repelli further teaches wherein a vertical column data structure and another layered form of data are generated from the operation of the configured sensor. (Repelli, Figure 6, 0048] Layers 410-420 of grid elements 402 become layers 460-470 of grid points 452. The distance between the grid points is referred to as the grid length. The grid length is representative of spatial resolution. Vertical levels determine the vertical resolution of the model. The result ofNWP is thus a three-dimensional grid of points 452 each of which is associated with an array of meteorological elements at a specific time or time step. The points are referred to as a grid points. The information (e.g., location, environmental element prediction data) associated with one or more such points is collectively referred to as grid data”).
Regarding Claim 11, Repelli teaches the system of claim 10,
Repelli is silent on wherein the processor is further configured to: identify information related to another sensor for another asset; identify, based on the information for the other sensor, stored layered data for the asset; analyze the information for the other sensor based on the stored layered data; and determine a configuration for the other sensor, the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor.
However, Cook teaches identifying information related to another sensor for another asset (Cook, Figures 2J, Step 286A, Identify sensor). identifying, based on the information for the other sensor, stored layered data for the asset;(Cook, Figure 2I, “[0510] As shown, the sensor as a service platform 280 may be connected to the cloud sensor resources 212. In one embodiment, the cloud sensor resources 212 may be used to manage and partition sensors. For example, the sensors (shown as sensors 284Al, 284A2, 284B1, 284B2, 284Nl, and 284N2) may be grouped based on a class (shown as sensor asset class 282A, sensor asset class 282B, and sensor asset class 282N).(…) a sensor asset class may include a grouping of sensors based on properties (such as attributes, etc.) and behaviors (such as permission levels, etc.)”) analyzing the information for the other sensor based on the stored layered data (Cook, Figure 2I, Could sensor resource 212); and determining a configuration for the other sensor, (Cook, Figure 2J, [0522] As such, the method 219 relates to the additional information that may be gleaned from a sensor. Such information may be provided to a sensor database, which in turn, may be used to update signatures at other sensors as well. In this manner, data obtained and analyzed from one sensor may be used to increase the intelligence with other sensors as well (and vice versa”) the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor. (Cook, Figure 2K-2L, [0523] The method 219 may be further enhanced by combining it with artificial intelligence (AI) and/or machine
learning capabilities. For example, per operation 288C, if the detected data is new, an AI system and/or machine learning system may be used to determine and compute what the detected data relates to (a new signature profile, etc.) [0526] Instruction is received to configure capability of one or more sensors. See operation 290C. Additionally, instructions are sent to update the one or more sensors based on the configured capability. In particular, the architecture 213 and architecture 215 may be especially pertinent in relating to updating and configuring sensors”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Regarding Claim 12, combination of Repelli and Cook teaches the system of
claim 11,
Repelli is silent on wherein the processor further comprising: configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities.
However, Cook teaches further comprising: configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities (Cook, Figure 7, Figure 8, [0675] The classification management system 810 may be in communication with new signature analysis 812 when it is determined by the classification management system 810 that a new signature is received from the sensor data 802. The new signature analysis 812 then communicates the data to relevancy to signatures 814 which is then assigned a confidence 816”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512], [0673]-[0677]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Regarding Claim 13, combination of Repelli and Cook teaches the system of
claim 12,
Repelli further teaches wherein a vertical column data structure, and another layered form of data are generated from the operation of the configured sensor. (Repelli, Figure 6, 0048] Layers 410-420 of grid elements 402 become layers 460-470 of grid points 452. The distance between the grid points is referred to as the grid length. The grid length is representative of spatial resolution. Vertical levels determine the vertical resolution of the model. The result ofNWP is thus a three-dimensional grid of points 452 each of which is associated with an array of meteorological elements at a specific time or time step. The points are referred to as a grid points. The information (e.g., location, environmental element prediction data) associated with one or more such points is collectively referred to as grid data”).
Regarding Claim 18, Repelli teaches the non-transitory computer-readable storage medium of claim 17,
Repelli is silent on further comprising: identifying information related to another sensor for another asset; identifying, based on the information for the other sensor, stored layered data for the asset; analyzing the information for the other sensor based on the stored layered data; and determining a configuration for the other sensor, the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor.
However, Cook teaches further comprising: identifying information related to another sensor for another asset (Cook, Figures 2J, Step 286A, Identify sensor). identifying, based on the information for the other sensor, stored layered data for the asset;(Cook, Figure 2I, “[0510] As shown, the sensor as a service platform 280 may be connected to the cloud sensor resources 212. In one embodiment, the cloud sensor resources 212 may be used to manage and partition sensors. For example, the sensors (shown as sensors 284Al, 284A2, 284B1, 284B2, 284Nl, and 284N2) may be grouped based on a class (shown as sensor asset class 282A, sensor asset class 282B, and sensor asset class 282N).(…) a sensor asset class may include a grouping of sensors based on properties (such as attributes, etc.) and behaviors (such as permission levels, etc.)”) analyzing the information for the other sensor based on the stored layered data (Cook, Figure 2I, Could sensor resource 212); and determining a configuration for the other sensor, (Cook, Figure 2J, [0522] As such, the method 219 relates to the additional information that may be gleaned from a sensor. Such information may be provided to a sensor database, which in turn, may be used to update signatures at other sensors as well. In this manner, data obtained and analyzed from one sensor may be used to increase the intelligence with other sensors as well (and vice versa”) the configuration comprising a modification to functionality of the other sensor that causes additional capabilities to be present on the other sensor. (Cook, Figure 2K-2L, [0523] The method 219 may be further enhanced by combining it with artificial intelligence (AI) and/or machine learning capabilities. For example, per operation 288C, if the detected data is new, an AI system and/or machine learning system may be used to determine and compute what the detected data relates to (a new signature profile, etc.) [0526] Instruction is received to configure capability of one or more sensors. See operation 290C. Additionally, instructions are sent to update the one or more sensors based on the configured capability. In particular, the architecture 213 and architecture 215 may be especially pertinent in relating to updating and configuring sensors”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Regarding Claim 19, combination of Repelli and Cook teaches the non-transitory computer-readable storage medium of claim 18,
Repelli is silent on further comprising configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities, wherein a vertical column data structure and another layered form of data are generated from the operation of the configured sensor.
However, Cook teaches further comprising configuring the other sensor based on the determined configuration; and operating the configured other sensor in relation to the other asset, the operation comprising the additional capabilities, wherein a vertical column data structure and another layered form of data are generated from the operation of the configured sensor. (Cook, Figure 7, Figure 8, [0675] “The classification management system 810 may be in communication with new signature analysis 812 when it is determined by the classification management system 810 that a new signature is received from the sensor data 802. The new signature analysis 812 then communicates the data to relevancy to signatures 814 which is then assigned a confidence 816”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Repelli’s’s method to incorporate multiple sensor asset capabilities upgrading method based on stored data comparison via machine learning as taught by Cook (Cook, abstract and [0505]-[0512], [0673]-[0677]). It would have been obvious to a person of ordinary skill to include the well-known sensor upgrading method with the benefit of machine learning system trained based on the first parameter and the pre-identified digital signature, in order to yield the predicted results of generating accurate upgraded sensor capabilities, yet with higher accuracy (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xiao et al. (US 12,141,138 B1) recites “In one example, a system can receive information about a data structure including a set of data entries. The system can generate a proxy data table including a set of columns. The system can use a data access layer to generate a mapping from the data entries to the columns. The system can receive an input to cause an operation to be performed on the data structure by performing the operation on the data structure. Generating a result can involve issuing read commands to
the data access layer to perform the operation on the data structure such that the data access layer obtains the associated data entries and provides them as responses to the read commands by performing a translation between the data
entries and the columns based on the mapping. The system can then output the result of the operation. (Abstract).
James Evans. (US 12,307,529 B1) The invention provides “An asset owner may be interested in determining risks associated with physical assets, such as to damage or other loss associated with the assets. Accurately identifying such risks may be useful in determining preventative actions that may be taken to reduce data or loss associated with the assets. The systems and methods described herein generally relate to automating a process of obtaining data regarding physical assets, such as from sensors associated with the assets, determining one or more risk indicators associated with the assets, and initiating some actions based on the determined risk indicators (abstract).
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/DILARA SULTANA/Examiner, Art Unit 2858
09/02/2023
/SON T LE/Primary Examiner, Art Unit 2858
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