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 .
Responsive to the communication dated 06/27/2023.
Claims 1 – 27 are presented for examination.
Priority
The ADS dated 06/27/2023 claims the benefit of provisional application US 63355984 dated 06/27/2022.
Information Disclosure Statement
No IDS provided.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign mentioned in the description: 100, referenced in par 29, line 1. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because
• Line 1, missing a “the” before “performance”.
• Line 2, missing a “for” before “grain stored”.
• Line 4, “move” should read “moving”.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
• In page 2, paragraph 4, line 3, “down time” should read “downtime”.
• In page 3, paragraph 1, line 11, “agriculture” should read “agricultural”.
• In page 4, paragraph 3, line 3, missing a “the” before “state of”.
• In page 8, paragraph 7, line 1, “that is” should read “that are”.
• In page 12, paragraph 3, line 3, “a top of container walls” should read “the top of the container walls”.
• In page 15, paragraph 3, line 5, “and modeling” should read “and the modeling”.
• In page 17, paragraph 2, line 13, “reading” should read “readings”. Line 14, “is” should read “are”.
• In page 19, paragraph 1, line 12, “prediction” should read “predictions". Line 13, “this error” should read “these errors”.
• In page 22, paragraph 3, line 2, “to fan” should read “to the fan”. Line 5, “to burner” should read “to the burner”.
• In page 24, paragraph 3, line 18, “it is” should read “they are”. “create” should read “to create”.
• In page 25, paragraph 2, line 15, “some lot of” should read “a lot of”.
• In page 26, paragraph 3, line 6, “such for example” should read “such as, for example”.
• In page 36, paragraph 2, line 4, “a specifying drying strategy” should read “a specific drying strategy”.
Appropriate correction is required.
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.
Claims 1 - 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Claim 1.
STEP 1: Yes. The claim recites: “A method”.
STEP 2A, PRONG ONE: Yes. The claim recites: “comprising:
analyzing the input data
developing a set of machine learning-based control models to estimate at least one manipulated variable representing a condition within the grain mass over time, wherein the set of machine learning-based control models include a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass, and validated by newly-collected input data that includes the additional variables, the testing model generating an estimated output at a specified time step representing a future time, and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs, and wherein the one or more machine learning-based models generate a system health profile representing both grain mass conditions and operational characteristics of the bin systems from the estimated output of the testing model, and
predicting the at least one manipulated variable for the grain mass from the system health profile;
” These limitations recite a mathematical concept because they involve establishing mathematical relationships between input data and output data (see MPEP 2106.04(a)(2)(A), first bullet example: a relationship between reaction rate and temperature, which relationship can be expressed in the form of a formula called the Arrhenius equation, Diamond v. Diehr; 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981)). In the instant application, the data manipulation for grain storage system monitoring is analogous to the application using Arrhenius equation. Analyzing the input data, and developing machine-learning based control models all involves establishing the mathematical relationships between the input data and the output data. Accordingly, the claim recites a mathematical concept, which constitutes an abstract idea.
STEP 2A, PRONG TWO: No. The claim does not recite additional elements that integrate the judicial exception into a practical application because the claim does not recite additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. While the claim recites:
receiving, as input data, information collected from a plurality of sensors that are associated
a) with auxiliary devices in one or more bin systems within or near a grain container,
b) with one or more sections of a grain mass stored in the grain storage container, and
c) with the grain storage container;
The examiner finds that these elements are merely extra-solution data gathering activities that are not a practical application because MPEP 2106.05(g) indicates that, for example, performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989).
In the instant application, a sensor gathering data from grain in a storage container is analogous to a clinical test. Further, the claim only requires that the sensor is “associated” with an “auxiliary device”, “one or more sections of a grain mass”, or “the grain storage container”. Merely associating a sensor with these elements simply links the use of the abstract idea to a field of use known as grain bins. (See MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.” The sixth bullet example: Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
The claim recites:
in a plurality of data processing elements within a computing environment that includes one or more processors and at least one computer-readable non-transitory storage medium having program instructions stored therein which, when executed by the one or more processors, cause the one or more processors to execute the plurality of data processing elements for monitoring and controlling the bin systems, by:
The examiner finds that these elements, such as processors and computer-readable non-transitory storage medium, are ordinary computer components wherein the recited data processing and evaluation are carried out. MPEP § 2106.05(f) gives an example: “In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. … The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that ‘apply it’ in a computer.” Merely using a computer as a tool to perform recited mathematical analysis, without reciting a particular technological implementation, does not integrate the judicial exception into a practical application.
The additional element controlling an operation of the auxiliary devices in the one or more bin systems from the system health profile recites a physical control result. But it does not recite how the mathematically generated system health profile is translated into a particular physical control action. The claim does not identify which auxiliary device is controlled, what operating parameter of the device is changed, what particular control signal is generated, or what relationship between a particular system health condition and a particular control action causes operation of the bin system to change. Rather, the limitation recites the desired result of controlling an operation based on the system health profile without specifying what control action is performed, which auxiliary-device operating parameter is changed, or how the calculated system health profile causes the auxiliary device to operate differently. MPEP § 2106.05(f) – “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’”. Thus, the controlling limitation broadly applies to the result of the mathematical analysis after the system health profile has been generated and does not meaningfully limit the judicial exception to a particular technological implementation.
MPEP 2106.05(a) – “During examination, the examiner should analyze the ‘improvements’ consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement.“ In the instant application, a review of the specification does not identify a technical explanation establishing that the recited mathematical modeling itself improves the operation of the grain-storage system, bin system, auxiliary devices, sensors, or computing system. In particular, the specification does not describe how generating the recited estimated output or system health profile produces a specific technological improvement in the operation of those devices.
Moreover, the claim does not recite a particular technological implementation such as a particular control technique for the bin system, a particular feedback mechanism, a particular manipulated control parameter, an actuator operation, a change in fan speed, aeration, temperature, humidity, airflow, ventilation, or another identified physical operating condition that improves the operation of the bin system, auxiliary devices, grain storage containers, or computing system itself. Nor does it recite a particular machine implementation, transformation, or other meaningful technological application that limits the mathematical concept. Instead, the sensor-data acquisition and generalized device-control limitations provide the technological environment in which the mathematical analysis are performed and used. Thus, the claim as a whole does not integrate the judicial exception into a practical application
STEP 2B. No. The additional elements, considered individually and as an ordered combination, do not recite additional elements that amount to significantly more than the recited judicial exception. As discussed above, the additional elements do not provide an improvement to the functioning of a computer or to another technological or technical field. MPEP § 2106.05(a) – “the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement … the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology.” Here, the specification and the claim have been considered together, but the claim itself does not reflect a particular technological improvement in the grain-storage system because it does not recite how the generated system health profile causes a particular auxiliary device to perform a particular control action.
The limitation of receiving information collected from a plurality of sensors merely supplies the data used in the recited mathematical analysis. The recited control of an operation of auxiliary devices merely applies the result of the mathematical analysis at a high level of generality without reciting a particular technological mechanism for translating the health profile into a particular physical control. Neither limitation recites a particular technological implementation that adds significantly more to the mathematical concept. Performing analysis on the input data and developing models constitutes abstract ideas.
The identified mathematical analysis limitations are part of the judicial exception and therefore cannot supply the inventive concept. The recited processors, computer-readable non-transitory storage medium, program instructions, and plurality of data processing elements do not provide an inventive concept. These elements merely provide the computing environment in which the recited mathematical analysis is performed. The claim does not recite a particular configuration or operation of these components that improves computer functionality or otherwise provides a technological improvement beyond implementation of the judicial exception.
The additional elements also do not amount to significantly more when considered as an ordered combination. The claim, as a whole, recites the generalized sequence of collecting sensor data, processing the data to evaluate grain and bin-system conditions, generating a system health profile, and using the resulting information to control the operation of auxiliary devices without reciting a particular relationship between a determined condition and a particular physical control action. The ordered combination does not recite a non-generic arrangement or additional technological feature that transforms the recited mathematical analysis into an inventive technological implementation.
Accordingly, considering the additional elements individually and as an ordered combination, the claim does not recite an inventive concept and does not amount to significantly more than the recited mathematical concept.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 2. The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the abstract idea. The claim recites: The method of claim 1, wherein the plurality of sensors that are associated with the grain storage container include one or more of fuel tank pressure sensors, line pressure sensors, amperage sensors, plenum pressure sensors, headspace sensors, and imaging sensors, fuel tank pressure sensors, line pressure sensors, amperage sensors, plenum pressure sensors, headspace sensors, and imaging sensors. Claim 2 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The additional limitations merely specify particular types of sensors used to collect the input data that is subsequently supplied to the recited mathematical analysis. The recited sensor limitations do not integrate the judicial exception into a practical application. The recited sensor types merely identify the sources from which the input data for the mathematical analysis may be obtained. The claim does not recite a particular sensor configuration, a modified sensing technique, improved sensor accuracy, a particular placement or interaction of the sensors, or another technological feature by which the recited sensors operate differently or provide an asserted technological improvement. Rather, the sensors supply the input information that is subsequently processed to generate the system health profile and predict the manipulated variables as recited in claim 1.
Thus, the recited sensors merely perform data-gathering that precedes the recited mental process and do not impose a meaningful limit on the judicial exception or otherwise integrate the judicial exception into a practical application.
At Step 2B, the additional limitations do not provide an inventive concept. The recitation of particular sensor types does not change the manner in which the mathematical model operates, does not recite an improvement to the sensors themselves, and does not specify a technological mechanism by which the sensor measurements cause a particular physical change in operation of the grain-storage system. Instead, the sensor limitations merely further characterize the data supplied to the mathematical analysis. Considered individually and in combination with the limitations of claim 1, the particular sensor types do not recite a particular technological implementation or other additional feature that amounts to significantly more the recited mental process.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 3. The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The claim recites: The method of claim 1, wherein the plurality of sensors associated with one or more sections of a grain mass stored in the grain container include static sensors or transient sensors that travel with the grain mass, the static sensors and transient sensors collecting information that represents one or more of relative humidity, temperature, and carbon dioxide in the grain mass. Claim 3 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The additional limitations merely specify whether the sensors are static or transient, including that the transient sensors travel with the grain mass, and the types of physical -condition information collected by those sensors. The recited sensors and the particular information collected by those sensors do not integrate the judicial exception into a practical application. The static and transient sensors collect relative humidity, temperature, and carbon-dioxide information for use as input to the mathematical modeling and generation of the system health profile recited in claim 1. The additional limitations primarily specify the manner and type of data gathering performed before the mathematical analysis.
The recitation that transient sensors travel with the grain mass does not, by itself, establish an improvement to sensor technology or another technical field. This limitation does not recite a particular sensor structure, movement mechanism, positioning technique, measurement technique, communication protocol, or interaction between the sensors and the grain mass that improves the operation of the sensors or the grain-storage system. MPEP 2106.05(a)(I), the fourth bullet example of the second group - Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone, TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747.
In the instant application, the transient sensors travel with the grain mass to gather information is analogous to the TLI Communications case. The recitation that transient sensors travel with the grain mass identifies how the input information is collected but does not recite a particular technological improvement in the manner in which the recited mathematical analysis and subsequent control is performed.
Thus, the static or transient nature of the sensors, their movement with the grain mass, and the particular categories of information collected merely further define the data acquisition used in connection with the same mathematical analysis recited in claim 1. These limitations do not recite a particular technological relationship by which the movement of the transient sensors, or the information they collect causes a particular change in operation of an auxiliary device or otherwise improves operation of the grain-storage system. Accordingly, the additional sensor limitations do not integrate the judicial exception into a practical application.
At Step 2B, these additional limitations do not amount to significantly more than the judicial exception. The static or transient nature of the sensors, their movement with the grain mass, and the particular categories of information collected merely define the data acquisition used in connection with the same mathematical process of claim 1. When these limitations are considered individually and in combination with the limitations of claim 1, they do not recite a particular technological mechanism by which the mathematical model, sensors, grain container, or auxiliary devices operate differently or are improved. Rather, they further specify the source and nature of the information supplied to the recited mathematical analysis.
Considering claim 3 as a whole, the additional sensor limitations do not recite a non-conventional sensing architecture, an improved movement or positioning mechanism, an improved measurement technique, or a particular technological relationship between the moving sensors, and the control of the grain-storage equipment that would amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 4. The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the abstract idea. The claim recites: The method of claim 1, wherein the at least one manipulated variable representing the condition within the grain mass over time is one or both of a moisture content of the grain mass and a drying rate of the grain mass. Claim 4 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The additional elements merely specifying moisture content and/or draying rate as the manipulated variable that is estimated or predicted. The limitations generally link the use of a judicial exception to a particular technological environment or field of use. MPEP §2106.05(h), the sixth example - Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
In the instant application, the data manipulation for grain storage system monitoring is analogous to an electric power grid monitoring. Specifying moisture content and/or drying rate as the manipulated variable merely limits the recited mathematical analysis to particular grain-mass conditions. The claim does not recite a particular technological manner of measuring, determining, or changing the moisture content or drying rate, or a particular relationship by which a predicted moisture content or drying rate courses a particular physical control action in the grain-storage system. Therefore, the limitations do not integrate the judicial exception into a practical application.
At Step 2B, the additional limitation does not provide an inventive concept. Considering claim 4 as a whole, specifying moisture content and/or drying rate as the manipulated variable does not change how the mathematical model operates, improve the recited computing components, or add a particular technological mechanism for controlling the grain-storage system. Instead, the limitation merely narrows the type of grain-condition information that is estimated or predicted by the mathematical analysis recited in claim 1. The claim does not recite a particular feedback relationship or control mechanism by which a predicted moister content or drying rate causes a specific change in fan operation, burner operation, airflow, temperature, humidity, ventilation, or another physical operating parameter off the grain-storage system.
Thus, considered individually and in combination with the limitations of claim 1, the additional limitation merely specifies the subject matter of the recited evaluation and prediction and does not recite a particular technological implementation that amounts to significantly more than the judicial exception. Accordingly, the additional limitation of claim 4 does not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 5. The claim does not recite additional elements that are significantly more than the abstract idea. The claim recites: The method of claim 1, wherein the auxiliary devices in the one or more bin systems include a fan, a burner, a vapor solenoid, and a modulating valve, and wherein the controlling the operation of the auxiliary devices in the one or more bin systems from the system health profile includes one or more of controlling a fan speed, controlling a fan power, controlling a burner rate, controlling a burner power, controlling the vapor solenoid, and controlling the modulating valve. Claim 5 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The claim recites the types of auxiliary devices including a fan, a burner, a vapor solenoid, and a modulating valve. The claim further recites the controls operations on those auxiliary devices, including fan speed/power, burner rate/power, vapor solenoid, and modulating valve. The examiner finds that these additional elements do not integrate the mathematical concept into a practical application. MPEP 2106.5(g), In Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978), the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula". 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was "post-solution activity").
In the instant application, control operations of the auxiliary devices via a machine-learning model is analogous to the alarm adjusting based on the mathematical calculation. Merely controlling auxiliary devices based on model outputs constitute post-solution activity, which is considered insignificant extra-solution activity.
At Step 2B, the additional limitations do not provide an inventive concept. As discussed above, the additional elements merely apply the result of the mathematical analysis through the recited control operations and do not add limitations that amount to significantly more than the judicial exception.
Considering claim 5 as a whole, including the identified auxiliary devices and control operations, the claim does not recite a particular technological solution or a particular technological mechanism by which the mathematically generated system health profile controls those devices. The recitation of specific equipment and operations limits the devices and operations to which the mathematical result applied but does not itself provide an inventive technological mechanism for carrying out the control. Accordingly, the additional limitations do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 6. The claim does not recite additional elements that are significantly more than the abstract idea. The claim recites: The method of claim 1, wherein the in-bin system is a stirator having one or more augers that move within the grain storage container to agitate the grain mass. Claim 6 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The additional limitation identifying the in-bin system as a stirator having one or more moving augers does not integrate the judicial exception into a practical application. MPEP §2106.05(a) states “In determining patent eligibility, examiners should consider whether the claim ‘purport(s) to improve the functioning of the computer itself’ or ‘any other technology or technical field.’ Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 225, 110 USPQ2d 1976, 1984 (2014).“ MPEP §2106.05(a)(I) further states “the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool.”. In court case Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016), the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id.
In the instant application, identifying the in-bin system as a stirator is analogous to the computers invoked merely as a tool because the claim merely identifies a stirator and augers performing their ordinary mechanical function of agitating grain. Unlike the claims in Enfish, the claim does not recite a particular modification to the stirator or augers, or a particular technological relationship between the mathematical analysis of claim 1 and the movement of the augers. The claim does not change or improve the baseline operation of grain storage technology itself. Therefore, the additional limitation does not integrate the judicial exception into a practical application.
At Step 2B, the additional elements, considered individually and as an ordered combination, do not recite additional elements that amount to significantly more than the recited judicial exception. When the stirator and auger limitations are considered individually and in combination with the limitations of claim 1, the claim still does not recite a particular control arrangement or technological implementation connecting the mathematical analysis to operation of the stirator. The claim does not recite how the system health profile or other output of the mathematical analysis causes or modifies movement of the augers, or how such movement is controlled in a particular technological manner.
Considering claim 6 as a whole, the additional stirator and auger limitations do not recite a particular technological mechanism by which the mathematical model controls, changes, or improves operation of the grain-storage equipment. The recitation that the augers physically move through and agitate the grain mass identifies a physical function of the stirator, but the claim does not recite that this physical function is performed according to a particular control rule, feedback relationship, or predicted output generated by the mathematical model. Thus, the claim does not recite a particular technological implementation connecting the judicial exception to the physical agitation of the grain mass. Accordingly, the additional limitations do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 7. The claim does not recite additional elements that are significantly more than the abstract idea. The claim recites: The method of claim 6, wherein the controlling the operation of the bin system includes at least one of controlling an auger speed, controlling an auger position, controlling a rotational arm speed of the stirator, controlling a fan state, controlling a fan speed, and controlling a burner temperature. Claim 7 recites the same judicial exception as claim 6 and is ineligible for substantially the same reasons discussed above with respect to claim 6.
The claim recites control operations on the bin-system, including controlling the speed and position of an auger, the rotational arm speed of a stirator, the state and speed of a fan, and a burner temperature. The examiner finds that these additional elements do not integrate the mathematical concept into a practical application. MPEP 2106.5(g), In Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978), the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula". 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was "post-solution activity").
In the instant application, control operations of bin-system devices via a machine-learning model is analogous to the alarm adjusting based on the mathematical calculation. The claim identifies particular parameters that may be controlled, such as auger speed or position, stirator rotational arm speed, fan state or speed, and burner temperature. The claim does not recite a particular technological manner in which the mathematically generated system health profile determines or changes any of those parameters. Thus, the additional limitations merely specify control operations performed using the result of the mathematical analysis, without reciting a particular technological implementation that meaningfully limits the judicial exception. Accordingly, the additional limitations constitute post-solution activity, which is considered insignificant extra-solution activity.
At Step 2B, the additional limitations do not provide an inventive concept. As discussed above, the additional elements merely apply the result of the mathematical analysis through the recited control operations and do not add limitations that amount to significantly more than the judicial exception.
Considering claim 7 as a whole, including the in-bin system control operations, the claim does not recite a particular technological solution or a particular technological mechanism by which the mathematically generated system health profile controls those devices. The recitation of specific equipment and operations limits the operations to which the mathematical result applied but does not recite how the system health profile is translated into a particular auger speed or position, stirator rotational arm speed, fan state or speed, or burner temperature. Accordingly, the additional limitations do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 8. The claim does not recite additional elements that are significantly more than the abstract idea. The claim recites: The method of claim 1, further comprising mapping the condition of the grain mass at an end of, or in close proximity to, the auxiliary devices as the auxiliary devices traverse through the grain mass, and wherein a location of augers, the location of the transient sensors, and the location of the sensors coupled to augers or other auxiliary devices, are triangulated by one or more controllers using the system health profile, to generate a map of the grain within the container. Claim 8 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The examiner finds that the additional limitations directed to triangulating locations and generating a map of grain conditions do not integrate the judicial exception into a practical application. Rather, these limitations further recite mathematical and information-processing operations applied to the sensor information and system health profile. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.” In the sixth bullet example - Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). The court explained that collecting information of specified content, analyzing that information, and presenting the results, without a particular assertedly inventive technology for performing those functions, falls within the abstract-idea category.
In the instant application, the generation of a spatial map of grain conditions is analogous to an electric power grid monitoring. Determining locations of augers, transient sensors, or sensors associated with auxiliary devices and generating a spatial map of grain conditions merely identifies the functional objectives of the invention. The claim does not recite a particular technological technique by which the triangulation or mapping is accomplished. The limitations requiring “triangulating” locations and “generating a map” state the results to be achieved, but do not recite the technological details by which those results are accomplished. The claim does not recite a particular arrangement of reference points, ranging technique, position-measurement technique, coordinate-determination algorithm, triangulation procedure, or other technological mechanism by which the locations of the sensor or auxiliary devices are determined. The claim does not recite a particular mapping technique by which the measured grain conditions and determined locations are converted into the resulting grain map. Thus, the claim recites the desired results of determining component locations and a spatial representation of grain conditions without reciting the particular technological features that produce an asserted improvement in location determination, sensing, mapping accuracy, or operation of the grain-storage system. The recited controller or controllers do not themselves establish a technological improvement.
The claim does not recite a particular controller architecture or modification to controller operation. The claim does not recite a particular controller configuration or technological modification to controller operation that enables an improved triangulation or mapping technique. Rather, the controller or controllers are used to perform recited location determination and mapping operations on the information associated with the grain mass and auxiliary devices. Therefore, the additional elements do not meaningfully limit the mathematical concept or provide a particular technological implementation of that concept.
At Step 2B, the additional limitations do not provide an inventive concept. When considered individually and in combination with the limitations of claim 1, the triangulation, location determination, and generation of a grain map further process and organize information derived from the sensors and system health profile. The claim does not recite a particular technological implementation for performing those operations or a particular technological relationship by which the resulting grain map causes the grain-storage equipment to operate in a particular manner.
Considering claim 8 as a whole, the recited combination of sensor-location determination, triangulation, and grain-condition mapping does not recite a particular positioning architecture, improved mapping technique, or defined control relationship in which the resulting map causes a particular physical control action in the grain-storage system. Accordingly, considering claim 8 as a whole, the additional limitations do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claim 9. The claim does not recite additional elements that are significantly more than the abstract idea. The claim recites: The method of claim 1, further comprising writing one or more data elements comprising the system health profile to a distributed ledger, and accessing the distributed ledger to perform one or more of monitoring the system health of the bin, initiating a reorder of parts for the in-bin systems, reporting conditions of the grain mass, and reporting conditions of the in-bin systems. Claim 9 recites the same judicial exception as claim 1 and is ineligible for substantially the same reasons discussed above with respect to claim 1.
The additional limitations of claim 9 merely specify storing data elements comprising the results of the recited mathematical analysis in a distributed ledger and subsequently accessing the stored information for monitoring, reordering, or reporting activities. The distributed ledger is recited as a mechanism for storing and providing subsequent access to the data generated by mathematical analysis. MPEP §2106.05(h), the sixth example - Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
In the instant application, data storing and retrieving for grain storage system monitoring is analogous to electric power grid monitoring. Similarly, storing and accessing the system health information in connection with a grain-storage system does not, by itself, impose a meaningful technological limitation on the mathematical analysis. Therefore, the limitations do not integrate the judicial exception into practical application.
Here, the distributed ledger is recited as the mechanism to which data elements comprising the mathematically generated system health profile are written and from which those data elements are subsequently accessed, thereby specifying where the resulting information is stored and retrieved. Thereby limiting the use of collected data to a distributed ledger. The claim does not recite a particular distributed-ledger architecture, consensus mechanism, validation procedure, transaction structure, synchronization technique, access-control technique, or other technological features by which the operation of the distributed ledger is improved. Rather, the distributed ledger is used to store and provide access to the information generated by the mathematical analysis recited in claim 1.
Accessing the stored data for monitoring the system health of the bin, reporting conditions of the grain mass, or reporting conditions of the in-bin systems likewise does not add a particular technological implementation. These limitations broadly recite uses of the stored system health information but do not recite a particular monitoring technique, reporting mechanism, or technological operation performed on the grain-storage equipment as a result of accessing the data. Accordingly, these additional elements do not integrate the judicial exception into a practical application.
At Step 2B, the additional limitations and their ordered combination do not provide an inventive concept. The distributed ledger merely stores and provides access to the data elements comprising the mathematically generated system health profile. The claim does not recite that the distributed ledger performs any function beyond storing and providing access to those data elements, nor does it recite a specific technological improvement in the manner in which the distributed ledger operates. Likewise, monitoring bin health, reporting grain-mass conditions, reporting in-bin-system conditions, and initiating parts reorder are recited at a high level of generality and do not add a particular technological mechanism that transforms the abstract mathematical analysis into a technological solution.
Considering the claim as a whole, the ordered combination merely recites generating the system health profile through the mathematical analysis of claim 1, writing resulting data elements to a distributed ledger, and subsequently accessing those data elements for monitoring reporting or initiating a parts reorder. The ordered combination merely uses the distributed ledger as the repository for the results of the mathematical analysis and subsequently uses the stored information for the recited monitoring, reporting, or reordering activities. Nothing in the claimed combination changes how the mathematical analysis is performed, improves the operation of the distributed ledger, or recites a particular technological implementation for controlling the grain-storage system. Accordingly, this combination does not add a particular technological implementation or inventive concept that amounts to significantly more than the judicial exception.
Therefore, it is concluded that the claim is not found eligible under 35 USC 101.
Claims 10 – 18 recite substantially the same grain storage monitoring and controlling process as claims 1 – 9, respectively, in system form.
Claim 10 further recites a computing environment including one or more processors and a computer-readable non-transitory storage medium having program instructions stored therein. The recited processors, storage medium, and program instructions merely provide the computing environment for performing the abstract mathematical operations, and do not add limitations that amount to significantly more than the judicial exception.
Claim 10 is ineligible for substantially the same reasons discussed above with respect to claim 1. The additional computing components merely provide the environment for performing the recited mathematical analysis and do not recite a particular improvement to computer functionality or another technological implementation. MPEP § 2106.04(d) referencing MPEP § 2106.05(f)(2) – “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. ” The additional elements, individually and as an ordered combination, therefore, do not amount to significantly more than the judicial exception.
Claims 11-18 recite limitations corresponding substantially to those of claims 2 – 9, respectively, and are ineligible for substantially the same reasons discussed above.
Claim 11 further specifies particular sensor types, but these limitations merely further define the source of data supplied to the mathematical analysis. The claim does not recite an improved sensor configuration, sensing technique, placement arrangement, or other technological features that change how the sensors operate or how the data are acquired.
Claim 12 further specifies static and transient sensors and the physical-condition information collected by those sensors, but these limitations merely further define the data gathering used by the mathematical analysis.
Claim 13 further specifies moisture content and/or drying rate as the manipulated variable but does not recite a particular technological mechanism for physically modifying those conditions.
Claim 14 further identifies particular auxiliary devices and control parameters, but does not recite a particular control rule, feedback relationship, or mechanism by which the mathematical output produces those control actions.
Claim 15 further recites a stirator and moving augers but does not recite a particular technological relationship between the mathematical output and operation of the stirator or augers.
Claim 16 further specifies particular physical operating parameters that may be controlled but does not recite how the mathematical output determines a particular change to those parameters.
Claim 17 further recites triangulating locations and generating a grain map but does not recite a particular technological technique for accomplishing the triangulation or mapping.
Claim 18 further recites writing system health profile data to a distributed ledger and accessing that data for monitoring, reporting, or initiating parts reorder, but does not recite particular improvement to distributed-ledger technology or a particular technological mechanism by which those uses are accomplished.
Accordingly, claims 10-18 do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claims 10 - 18 are not found eligible under 35 USC 101.
Claims 19 – 27 recite substantially the same grain storage monitoring and controlling process as claims 1 – 9, respectively, in another method form.
Claim 19 is ineligible for substantially the same reasons discussed above. The claim recites substantially the same mathematical modeling, generation of a system health profile, prediction of a system health profile, prediction of at least one manipulated variable, and control of the auxiliary devices as claim 1. The additional elements, individually and as an ordered combination, therefore, do not amount to significantly more than the judicial exception.
Claims 20 – 27 recite limitations corresponding substantially to those of claims 2 – 9, respectively, and are ineligible for substantially the same reasons discussed above.
Claim 20 further specifies particular sensor types, but these limitations merely further define the sources of data supplied to the mathematical analysis.
Claim 21 further specifies static and transient sensors and the physical condition information collected by those sensors, but these limitations merely further define the data gathering used by the mathematical analysis.
Claim 22 further specifies moisture content and/or drying rate as the manipulated variable but does not recite a particular technological mechanism for physically modifying those conditions.
Claim 23 further identifies particular auxiliary devices and control parameters, but does not recite a particular control rule, feedback relationship, or mechanism by which the mathematical output produces those control actions.
Claim 24 further recites a stirator and moving augers but does not recite a particular technological relationship between the mathematical output and operation of the stirator or augers.
Claim 25 further specifies particular physical operating parameters that may be controlled but does not recite how the mathematical output determines a particular change to those parameters.
Claim 26 further recites triangulating locations and generating a grain map but does not recite a particular technological technique for accomplishing the triangulation or mapping.
Claim 27 further recites writing system health profile data to a distributed ledger and accessing that data for monitoring, reporting, or initiating parts reader, but does not recite a particular improvement to distributed-ledger technology or a particular technological mechanism by which those uses are accomplished.
Accordingly, claims 19-27 do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
Therefore, it is concluded that the claims 19 - 27 are not found eligible under 35 USC 101.
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 1, 3, 4, 10, 12, 13, 19, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 (US 20190265082 A1) in view of Rhodes_1997 (US 5651193 A) in view of Jin_2021 (CN 113566557 A, translation provided).
Claim 1. Zafar_2019 makes obvious A method, comprising: receiving, as input data, information collected from a plurality of sensors (par 231: “data collected by one or more sensor units 105 are analyzed by an algorithm to detect anomalous events … The data analyzed to determine the presence of an anomaly comprise data corresponding to any environmental variable discussed in the present disclosure, including temperature, humidity, carbon dioxide, oxygen, phosphine, ethylene, sound, movement, pressure and combinations thereof.”; par 40: “The method comprises receiving the detected environmental data set from the gateway.“) that are associated (par 12: “a sensor unit located in a grain storage environment that detects an environmental variable from the grain storage environment,”; par 218: “sensor units 605A, 605B, and 605C are each located a different area within a grain storage environment … the temperature values recorded by sensor unit 605A in one section of the grain mass are compared to … sensor units 605B and 605C, each located in a different section of the grain mass.”; par 55: “The one or more sensors comprise at least one of the following: a temperature sensor, a humidity sensor, an oxygen sensor, a carbon dioxide sensor, a phosphine sensor, an acoustic sensor, an ethylene sensor, a positioning sensor, an accelerometer, and a pressure sensor.”);
analyzing the input data in a plurality of data processing elements within a computing environment (par 234: “temperature data are collected from one or more sensor units 105, … and is transferred to gateway 102 and uploaded to the cloud computing system 101 … Each set of temperature data is then analyzed by an algorithm depicted in FIG. 10. ”) that includes one or more processors and at least one computer-readable non-transitory storage medium having program instructions stored therein (par 104: “A processor 203 executes instructions in memory 202 as well as reads and writes data to and from memory 202.”; par 127: “Once in the cloud, the data are stored and analyzed using one or more machine-learning trained algorithms … Users can access this analytical data using a computer or mobile phone that can connect to a native and/or web-based application that presents a variety of outputs, including data, quality scores, trend lines, and recommendations.”; par 131: “The cloud computing system stores 101 and analyzes the data received from the gateway 102.”) which, when executed by the one or more processors, cause the one or more processors to execute the plurality of data processing elements for monitoring and controlling the bin systems (par 127: "The present patent document discloses sensor systems for monitoring and predicting the quality of grain in a storage environment."; par 230: “ the system will automatically initiate a downstream process in response to the determination that the storage environment is conducive to spoilage. ”), by:
developing a set of machine learning-based control models to estimate at least one manipulated variable representing a condition within the grain mass over time, (par 127: “Once in the cloud, the data are stored and analyzed using one or more machine-learning trained algorithms. The algorithms thereby generate a variety of analytical data including a Grain Storage Quality Index ("GSQI") (an objective measure of quality for the grain in storage) score, environmental trends (e.g., temperature trend line over time), environmental value predictions, and probabilities of an anomalous event (e.g., probability of developing a hotspot within the grain).”), and
predicting the at least one manipulated variable for the grain mass from the system health profile (par 129: “A machine-learning trained algorithm, utilizing an initial GSQI value for the grain in storage, is then applied to the environmental data to generate predicted GSQI value that estimates the quality of the grain in storage at the current moment and/or one or more time points in the future. The GSQI value is universal in that it allows an accurate direct comparison of the quality two batches of grain”); and
Zafar_2019 does not explicitly teach a plurality of sensors that are associated a) with auxiliary devices in one or more bin systems within or near a grain container or wherein the set of machine learning-based control models include a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass, and validated by newly-collected input data that includes the additional variables, the testing model generating an estimated output at a specified time step representing a future time, and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs, or controlling an operation of the auxiliary devices in the one or more bin systems from the system health profile.
Rhodes_1997; however, renders obvious a plurality of sensors that are associated a) with auxiliary devices in one or more bin systems (col 9, line 8: “At the opposite end of the housing from the fan is disposed an air switch sensor 56 for sensing whether air is flowing from the fan. Such sensors may be either velocity or pressure sensors, as desired.”; line 21: “A sensor 57A is also disposed along the propane line to sense when the temperature of the vapor exceeds a predetermined limit.”; col 27, line 60: “’Burner 1 Vapor High Temperature’-sensor associated with that burner has indicated that the temperature for that vapor is too hot”; line 63: “’Burner 1 Warning Flame Not Detected’-flame sensor associated with that burner has failed to detect a burner flame …”; line 28: “’Fan 1 Housing High Temperature’-temperature high limit sensor located on the fan/burner housing has opened”; col 29, line 21: “The bin 301 has a fan/heater unit 49 … mounted on the bin sidewall 303 …”; line 33: “A number of sensors are located through the bin and the fan/heater unit 49 to monitor various temperatures and other conditions which indicate the status of the grain being dried.”).
Zafar_2019 and Rhodes_1997 are analogous art because they are from the same field of endeavor called grain-storage monitoring and environmental control. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997. The rationale for doing so would have been that Zafar_2019 teaches collecting data related to grain storage conditions from the sensors associated with the grain mass and grain storage container, analyzing the input data using machine learning algorithms, and predicting the grain mass conditions. Rhodes_1997 teaches grain storage sensors associated with auxiliary devices. Therefore, it would have been obvious to combine the data collection from sensors associated with the grain mass and the grain storage container, analysis, and grain-condition prediction techniques taught by Zafar_2019 with the storage sensors associated with auxiliary devices taught by Rhodes_1997 for the benefit of collecting more comprehensive data from the grain storage, and improving the storage monitoring and control operations, and in turn improving the ability to maintain the grain storage at desired conditions.
Zafar_2019 in view of Rhodes_1997 does not explicitly teach wherein the set of machine learning-based control models include a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass, and validated by newly-collected input data that includes the additional variables, the testing model generating an estimated output at a specified time step representing a future time, and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs, or controlling an operation of the auxiliary devices in the one or more bin systems from the system health profile.
Jin_2021; however, renders obvious wherein the set of machine learning-based control models include a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass (page 2, par 8: “A grain dryer intelligent control method based on deep neural network, comprising the following steps: step one, collecting grain dryer working state parameter by sensor in batch feed outlet the grain water content and drier grain discharging waiting time, forming a plurality of groups of first sample data set; … step two, taking one part of the multiple groups of first sample data set as the first training set; establishing a first depth neural network model, and using the first training set to train the first depth neural network to obtain the grain discharging waiting time prediction model …”; page 3, par 2: “step three, inputting the current grain dryer working state parameter and grain target water content into the grain discharging waiting time prediction model to obtain the predicted grain discharging waiting time; … step four, … establishing a second depth neural network model, and using the second training set to train the second depth neural network to obtain the grain feed outlet content prediction model; … inputting the current grain dryer working state parameter into the feed outlet grain water content prediction model to obtain the predicted grain water content; …”; Note: A neural network model is a machine learning model.), and validated by newly-collected input data that includes the additional variables (page 6, par 10: “Model Verification: ”; par 15: “In the subsequent test, with the increase of the data quantity, the model is self-optimized, the precision is further improved. When the data amount reaches 4000 groups, the depth neural network model precision is higher than the regression model precision. through the test example further explanation of the invention based on deep neural network model, the grain drying process control method can [achieve] high precision the control of the grain drying process.”), the testing model generating an estimated output at a specified time step representing a future time (page 6, par 6: “As shown in FIG. 3, the system comprises 4 modules, respectively is a model training module, a data prediction module, a model updating module and a control module. a model training module: mainly finishing the model training task; … based on the initial data set … respectively training two models, and storing … a data prediction module: It mainly finishes the task of predicting two output items grain-discharging port grain water content and grain-discharging waiting time.”;), and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs (par 8: “collecting 13 input items (grain dryer working state parameter) new data by each sensor”; par 9: “a model updating module: mainly finishing model termly self-learning, self-optimizing task. adding the new data … into the initial data set at last row; when the new data amount reaches a predetermined amount … starting the model training module, updating the two models.”)
Jin_2021 further renders obvious controlling an operation of the auxiliary devices in the one or more bin systems from the system health profile (page 3, par 3: “step four, controlling the grain discharging waiting time of the grain dryer according to the predicted grain discharging waiting time.”).
Zafar_2019 and Rhodes_1997 and Jin_2021 are analogous art because they are from the same field of endeavor called grain-storage monitoring and environmental control. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021. The rationale for doing so would have been that Zafar_2019 in view of Rhodes_1997 teaches collecting data related to grain storage conditions from the sensors, analyzing the input data using machine learning algorithms, and predicting the grain mass conditions. Jin_2021 teaches training and updating the machine learning model using historical data and newly collected data, controlling the operation to maintain the grain mass at the desired conditions based on the predicted results from the machine learning model. Therefore, it would have been obvious to combine the data collection, analysis, and grain-condition prediction techniques taught by Zafar_2019 in view of Rhodes_1997 with the training and updating machine learning models, the grain mass condition predicting method, and the grain-dryer control techniques taught by Jin_2021 for the benefit of improving the storage monitoring and control operations, maintaining the accuracy and adaptability of the predictive models as operating conditions change, and in turn improving the ability to maintain the grain storage at desired conditions.
Claim 3. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations in claim 1. Zafar_2019 further renders obvious wherein the plurality of sensors associated with one or more sections of a grain mass stored in the grain container include static sensors or transient sensors that travel with the grain mass, the static sensors and transient sensors collecting information that represents one or more of relative humidity, temperature, and carbon dioxide in the grain mass (par 156: “Sensor units 105 comprise internal sensors 307A-Z that detect an environmental variable and generate data corresponding to that environmental variable. As described above, exemplary environmental values include the following: temperature, humidity level, carbon dioxide concentration, oxygen concentration, phosphine concentration, ethylene concentration, sound levels, and movement.”; par 157: “The sensor unit 105 may comprise additional sensors as well, which are not necessarily used to detect environmental variables. For example, the sensor unit 105 further comprises a positioning sensor. The positioning sensor can determine the location of the sensor unit 105 … The accelerometer can be used to detect movement in the grain."; par 276: “This exemplary sensor system comprises two enclosed sensor units 1205, analogous to enclosed sensor unit 700. Enclosed sensor units 1205 are configured, for example, to detect temperature and humidity values inside grain silo 1210, where they are deployed.”; par 59: “In another embodiment, the one or more external sensors comprises at least one of the following: an external temperature sensor, an external humidity sensor, an external carbon dioxide sensor … an external positioning sensor, an external accelerometer, and an external pressure sensor.”; Note: The claim recites “static sensors or transient sensors” in the alternative. Therefore, teaching either the static-sensor alternative or the transient-sensor alternative is sufficient to meet this limitation. Zafar_2019 teaches at least the static-sensor alternative.).
Claim 4. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations in claim 1. Jin_2021 further teaches wherein the at least one manipulated variable representing the condition within the grain mass over time is one or both of a moisture content of the grain mass and a drying rate of the grain mass (page 5, par 11: “The online grain moisture meter group comprises two PT2703 type online grain moisture meter (M), respectively is mounted on the grain inlet of the lifter and the grain discharging port of the continuous grain drier, for measuring the initial water content of the rice and the water content of the machine in real time.”; page 10, par 16: “In the present invention, based on two depth neural network model to obtain the predicted grain discharging port grain water content and predicting the grain discharging waiting time, further comprising: according to the predicted grain outlet grain moisture content and actually measuring the difference rate of grain water content of grain outlet, determining grain discharging waiting time of grain dryer.”; Note: the claim recites “one or both of a moisture content of the grain mass and drying rate of the grain mass” in the alternative. Therefore, teaching the moisture-content alternative is sufficient to meet the limitation.).
Claim 10 corresponds to claim 1 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 1, with the corresponding system limitations being met by the disclosed processors, memory, sensors, control components, and other system elements discussed above.
Claim 12 corresponds to claim 3 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 3.
Claim 13 corresponds to claim 4 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 4.
Claim 19 corresponds to claim 1 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 1, with the corresponding method limitations being met by the disclosed sensing, data analysis, model development, prediction, and control operations discussed above.
Claim 21 corresponds to claim 3 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 3.
Claim 22 corresponds to claim 4 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 for substantially the same rationale set forth above with respect to claim 4.
Claims 2, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Rogoschewsky_2022 (US 20220170696 A1).
Claim 2. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations of claim 1. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 does not teach wherein the plurality of sensors that are associated with the grain storage container include one or more of fuel tank pressure sensors, line pressure sensors, amperage sensors, plenum pressure sensors, headspace sensors, and imaging sensors
Rogoschewsky_2022; however, renders obvious wherein the plurality of sensors that are associated with the grain storage container include one or more of fuel tank pressure sensors, line pressure sensors, amperage sensors, plenum pressure sensors, headspace sensors, and imaging sensors (par 64: “the control unit 10 also features a dedicated dual-input terminal 33 to which a primary plenum sensor cable 16 … a tubular air hose 16B through which air from the storage bin plenum is communicable to an onboard pressure sensor contained in the housing 26 of the main control unit 10.”; Note: The claim recites the listed sensor types in the alternative using “one or more of”. Therefore, teaching one of the sensors is sufficient to meet this limitation. Rogoschewsky_2022 teaches plenum pressure sensors alternative.).
Zafar_2019 and Rhodes_1997 and Jin_2021 and Rogoschewsky_2022 are analogous art because they are from the same field of endeavor called grain storage monitoring and environmental control. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021 and Rogoschewsky_2022. The rationale for doing so would have been that Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 teaches collecting data related to grain storage conditions, building and updating machine-learning-based models using historical and current data, predicting the grain mass conditions, and controlling the auxiliary devices to maintain the grain storage at desired conditions. Rogoschewsky_2022 teaches a plenum pressure sensor associated with the grain storage container for sensing pressure within the storage-bin plenum. Therefore, it would have been obvious to combine the grain-mass condition data collection, prediction, and control taught by Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 with the plenum pressure sensor associated with the grain storage container taught by Rogoschewsky_2022 for the benefit of obtaining pressure information from the storage-bin plenum, thereby providing additional information concerning the operating condition of the grain storage system, improving the grain-storage monitoring and control operations, and in turn improving the ability to maintain the grain-storage at desired conditions.
Claim 11 corresponds to claim 2 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Rogoschewsky_2022 for substantially the same rationale set forth above with respect to claim 2.
Claim 20 corresponds to claim 2 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Rogoschewsky_2022 for substantially the same rationale set forth above with respect to claim 2.
Claims 5, 14, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of McClaren_1981 (US 4270280).
Claim 5. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations of claim 1. Rhodes_1997 further renders obvious wherein the auxiliary devices in the one or more bin systems include a fan, a burner, a vapor solenoid, and the operation of the auxiliary devices in the one or more bin systems from the system health profile includes one or more of controlling a fan speed, controlling a fan power, controlling a burner rate, controlling a burner power, controlling the vapor solenoid, (col 9, line 4: “A fan 53 is mounted within housing 51 proximate the intake end thereof.”; col 9, line 11: “A burner 57 is mounted within housing 51 downstream from fan motor 55.”; col 9, line 18: “As shown in FlG. 6, the flow of vaporized propane to burner 57 is controlled by a 20 solenoid valve S which in turn is controlled by the control system for the dryer in a manner described hereinafter. A sensor 57 A is also disposed along the propane line to sense when the temperature of the vapor exceeds a predetermined limit. This sensor is also connected by a suitable electrical 25 connection (not shown) to the control circuit. When a high vapor temperature condition occurs, the control circuit in response to the signal from sensor 57 A shuts down the dryer.”; col 9: line 48: “The fan/heater units 49 may thus be controlled so as to provide heated air of a desired temperature 50 and flow rate to the various zones within the drying chamber such that the air discharged from the drying chamber”; Note: The claim recites the control operations using “one or more of”. Rhodes_1997 recites fan unit control, noting that a standard fan allows basic on/off operation and speed adjustment. Consequently, both the fan power and fan speed can be controlled. Therefore, Rhodes_1997 at least renders obvious the fan speed, fan power, and vapor solenoid control alternatives).
Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 does not explicitly teach a modulating valve or controlling the modulating valve.
McClaren_1981; however, renders obvious a modulating valve and controlling the modulating valve (col 9, line 37: “Propane gas to the burner 25.1 is controlled by a valve 39 which is preferably an on/off valve and also accomplishes a modulating function to vary the quantity of propane gas permitted to reach the burner 25.1 from the source 39.1.”).
Zafar_2019 and Rhodes_1997 and Jin_2021 and McClaren_1981 are analogous art because they are from the same field of endeavor of grain-storage monitoring and control. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021 and McClaren_1981. The rationale for doing so would have been Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 teaches collecting data related to grain storage conditions, building and updating machine-learning-based models using historical and current data, predicting the grain mass conditions, and controlling the auxiliary devices to maintain the grain storage at desired conditions. Rhodes_1997 further teaches that such auxiliary devices include a fan, a burner, and a vapor solenoid, including control of the vapor solenoid. McClaren_1981 teaches that the burner-control system further includes a valve having a modulating function for varying the quantity of propane supplied to the burner. Therefore, it would have been obvious to combine the grain-mass condition data collection, prediction, and control with the auxiliary devices including a fan, a burner, a vapor solenoid, and their associated control taught by Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 with a modulating valve and its control taught by McClaren_1981 for the benefit of providing variable control of the fuel supplied to the burner, thereby permitting more precise control of burner operation and drying conditions, and improving control of the auxiliary devices for better maintaining desired grain storage conditions.
Claim 14 corresponds to claim 5 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of McClaren_1981 for substantially the same rationale set forth above with respect to claim 5.
Claim 23 corresponds to claim 5 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of McClaren_1981 for substantially the same rationale set forth above with respect to claim 5.
Claims 6, 7, 15, 16, 24, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 (US 4854719) .
Claim 6. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations in claim 1. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 does not teach wherein the in-bin system is a stirator having one or more augers that move within the grain storage container to agitate the grain mass.
Sukup_1989; however, renders obvious wherein the in-bin system is a stirator having one or more augers that move within the grain storage container to agitate the grain mass (col 3, line 22, “Reference numeral 10 generally designates grain stirring apparatus constructed in accordance with the principles of this invention. The illustrated apparatus 10 comprises a stirrer 11 which extends downwardly into grain in a grain bin. The stirrer 11 is driven from an electric motor 12 to rotate a spiral blade 13 thereon in a direction as indicated by arrow 14, such that grain engaged by the blade 13 will be moved upwardly. Reference numeral 15 generally designates a traversing mechanism which is provided for moving the stirrer 11 horizontally through the grain while it is rotated. The illustrated mechanism is operative to effect reciprocable movement of the stirrer 11 in a first horizontal direction, back and forth between a first pair of limit positions adjacent opposite sides of a bin, while also effecting reciprocable movement of the stirrer in a second horizontal direction transverse to the first, back and forth between a second pair of limit positions adjacent opposite ends of a bin. Over a period of time, the stirrer 11 may be moved through substantially all the grain in a rectangular bin for efficient mixing and aerating of the grain.”; col 8, claim 5: “Grain stirring apparatus as defined in claim 4, said stirrer including, auger means for effecting upward movement of grain engaged thereby and for thereby exerting an additional downward force on said carrier structure and said thrust bearing surfaces.”).
Zafar_2019 and Rhodes_1997 and Jin_2021 and Sukup_1989 are analogous art because they are from the same field of endeavor of grain-storage monitoring and environmental control. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021 and Sukup_1989. The rationale for doing so would have been that Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 teaches collecting data related to grain storage conditions, building and updating machine-learning-based models using historical and current data, predicting the grain mass conditions, and controlling the auxiliary devices to maintain the grain storage at desired conditions. Sukup_1989 teaches a grain stirring apparatus having an auger that moves through the grain within the grain bin to mix and aerate the grain. Therefore, it would have been obvious to combine the grain-mass condition data collection, prediction, and control taught by Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 with the movable grain stirring apparatus having an auger taught by Sukup_1989 for the benefit of mixing and aerating the stored grain, thereby improving the uniformity of grain conditions and the ability to maintain desired grain storage conditions.
Claim 7. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 renders obvious all the limitations in claim 6. Rhodes_1997 further renders obvious wherein the controlling the operation of the bin system includes at least one of controlling an auger speed, controlling an auger position, controlling a rotational arm speed of the stirator, controlling a fan state, controlling a fan speed, and controlling a burner temperature (claim 3: “control means for controlling operation of the fan and heater assembly … said control means being responsive to the airflow sensor to start the fan only when the airflow sensor indicates no airflow from the fan and heater assembly. said control means being further responsive to the airflow sensor to start the heater only if the airflow sensor indicates the flow of air from the fan and heater assembly within a predetermined length of time after the fan is started.”; Note: Claim 7 recites the listed control operations using “at least one of”. Therefore, teaching controlling operation of the fan and heater assembly is sufficient to meet this limitation.).
Claim 15 corresponds to claim 6 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 for substantially the same rationale set forth above with respect to claim 6.
Claim 16 corresponds to claim 7 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 for substantially the same rationale set forth above with respect to claim 7.
Claim 24 corresponds to claim 6 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 for substantially the same rationale set forth above with respect to claim 6.
Claim 25 corresponds to claim 7 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Sukup_1989 for substantially the same rationale set forth above with respect to claim 7.
Claims 8, 17, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Johnson_2021 (US 20210276794 Al).
Claim 8. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations in claim 1. Zafar_2019 further renders obvious (par 191: “ … the enclosed sensor unit 700 may include one or more positioning sensors to determine position of the sensor unit 700, (e.g., GPS). In some embodiments, position may be determined through triangulation, (e.g., by measuring angle to strongest signal for three or more wireless nodes that are in known locations).”).
Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 does not teach further comprising mapping the condition of the grain mass at an end of, or in close proximity to, the auxiliary devices as the auxiliary devices traverse through the grain mass … to generate a map of the grain within the container.
Johnson_2021; however, renders obvious further comprising mapping the condition of the grain mass at an end of, or in close proximity to, the auxiliary devices as the auxiliary devices traverse through the grain mass … to generate a map of the grain within the container (par 92: “a robot 100 which includes a processor 102, a memory 103, and an auger-based drive system (e.g., augers 403), obtains a first measurement of an angle of slope of a portion of piled granular material in a bulk store”; par 93: “the robot 100 traverses the portion of piled granular material … through agitation of the portion of piled granular material by auger rotation of the auger-based drive system.”; par 99: “during traversal of the portion … of piled granular material by robot 100, a sensor 120 of robot 100 … capture a measurement of a characteristic of the portion of piled granular [material]. Some example characteristics include … temperature, humidity, moisture, gas composition, electrostatic nature, and/or electrochemical nature. … the captured measurement of a characteristic is paired with a location of robot 100 at the time of capture of the measurement. Such paired data can be used to create a characteristic map of the piled granular material which is traversed by robot 100.”; par 102: “a temperature sensor 233, infrared sensor 236, or infrared camera 108 of robot 100 is used to capture a temperature measurement of the portion of piled granular material during the traversal of the portion of piled granular material … the captured measurement of a characteristic is paired with a location of robot 100 at the time of capture of the temperature measurement. Such paired data can be used to create a heat map of the piled granular material which is traversed by robot 100.”; Note: Johnson_2021 teaches mapping the condition of the grain mass as an auger-driven device traverses through the grain mass, and measuring a condition of the grain mass during traversal and associating the measured condition with the location at which the measurement is obtained to generate a map of the grain.).
Zafar_2019 and Rhodes_1997 and Jin_2021 and Johnson_2021 and are analogous art because they are from the same field of endeavor of monitoring conditions of stored grain using sensors and associated grain-handling or monitoring devices. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021 and Johnson_2021. The rationale for doing so would have been Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 teaches collecting data related to grain storage conditions, building and updating machine-learning-based models using historical and newly collected data, predicting the grain mass conditions, controlling the auxiliary devices to maintain the grain storage at desired conditions, and determining sensor location by triangulation. Johnson_2021 teaches measuring grain conditions as an auger-driven device traverses through the grain mass, associating the measurements with respective locations of the device, and using the paired measurements and locations to generate a characteristic map of the grain mass. Therefore, it would have been obvious to combine the sensor-location determination and grain-condition monitoring techniques taught by Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 with the location-correlated grain-condition mapping technique taught by Johnson_2021 for the benefit of generating a spatial map of grain conditions within the container, thereby improving identification of localized grain conditions and facilitating more effective monitoring and control of the stored grain.
Claim 17 corresponds to claim 8 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Johnson_2021 for substantially the same rationale set forth above with respect to claim 8.
Claim 26 corresponds to claim 8 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Johnson_2021 for substantially the same rationale set forth above with respect to claim 8.
Claims 9, 18, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Liu_2020 (CN 111242530 A).
Claim 9. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 renders obvious all the limitations in claim 1. Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 does not teach further comprising writing one or more data elements comprising the system health profile to a distributed ledger, and accessing the distributed ledger to perform one or more of monitoring the system health of the bin, initiating a reorder of parts for the in-bin systems, reporting conditions of the grain mass, and reporting conditions of the in-bin systems.
Liu_2020; however, renders obvious further comprising writing one or more data elements comprising the system health profile to a distributed ledger (page 20, par 1: “… by various kinds of information sensor device, internet of things multifunctional gateway, collecting, gathering quality data of different grain variety and number data. temperature data, humidity data, situation data and gas data such as by container grain reserve state tagging module generates a storage state tracking code, the storage state of the generated tracking code as a storage state after transaction encryption issued to blockchain module.”; Note: a blockchain is a type of distributed ledger.), and accessing the distributed ledger to perform one or more of monitoring the system health of the bin, initiating a reorder of parts for the in-bin systems, reporting conditions of the grain mass, and reporting conditions of the in-bin systems (page 19, par 11: “each peripheral node needed by reading the block chain layer synchronous state of block chain; the verification node in the local data block chain copy retrieved data position information corresponding to the user signature …”; par 17: “the container in the grain storage state after verifying the tracking code in encrypted stored in the block chain, it can realize container grain in the logistics process data acquisition and tracking.”; Note: Claim 9 recites the listed uses of distributed ledger using “one or more of”. Therefore, the prior art needs only teach at least one of the recited uses. Liu_2020 renders obvious storing grain-storage-condition data on a blockchain and accesses the blockchain to retrieve and track grain-storage-state information, thereby teaching at least the reporting/monitoring alternative.
Although Liu_2020 does not expressly call the stored information a “system health profile”, Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 already renders obvious generating the claimed system health profile comprising grain-mass conditions and operational characteristics of the bin system. Liu_2020 teaches storing and retrieving comparable grain-storage-condition information using a blockchain. Thus, the combination renders obvious writing data elements comprising the claimed system health profile to the distributed ledger and subsequently accessing those data elements for monitoring or reporting.).
Zafar_2019 and Rhodes_1997 and Jin_2021 and Liu_2020 are analogous art because they are from the same field of endeavor of monitoring, managing, or recording conditions associated with stored grain. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zafar_2019 and Rhodes_1997 and Jin_2021 and Liu_2020. The rationale for doing so would have been Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 teaches collecting data related to grain storage conditions, building and updating machine-learning-based models using historical and current data, predicting the grain mass conditions, generating a system health profile, and controlling the auxiliary devices to maintain the grain storage at desired conditions. Liu_2020 teaches storing grain-storage condition information in a blockchain and subsequently accessing the blockchain to retrieve and track the stored information. Therefore, it would have been obvious to combine the grain-mass condition data collection, prediction, system health profile generation, and control taught by Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 with the blockchain storage and retrieval of grain-storage-condition information taught by Liu_2020 for the benefit of providing secure and traceable storage and retrieval of grain-storage condition information, thereby improving the reliability, accessibility, and tracking of grain-storage data.
Claim 18 corresponds to claim 9 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Liu_2020 for substantially the same rationale set forth above with respect to claim 9.
Claim 27 corresponds to claim 9 and is rejected over Zafar_2019 in view of Rhodes_1997 in view of Jin_2021 in view of Liu_2020 for substantially the same rationale set forth above with respect to claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIANG LU whose telephone number is (571)270-1484. The examiner can normally be reached M-F, 9am to 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Q.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
Prosecution Insights