DETAILED ACTION
Status of Claims
This is a non-final action in reply to the application filed on November 8, 2023.
Claims 1-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statements filed on 5/30/2024 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-9 falls within statutory class of a machine, claims 10-18 falls within statutory class of a process and claims 19-20 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception. If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
a processing unit;
a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising:
receiving, using a processing unit, an indication that a current instance of an agricultural operation has begun in a geographic area;
during the current instance of the agricultural operation, receiving via an application programming interface, task characteristics of the agricultural operation;
generating a first task data structure that includes the task characteristics, a field identifier associated with the geographic area, and a task identifier;
storing the first task data structure in a database as associated with a first time period;
accessing a second task data structure for the agricultural operation associated with a second time period, the second time period being before the first time period;
inputting the first task data structure and second task data structure into a difference model; receiving an output from the difference model identifying a difference for a first characteristic of the task characteristics between first task data structure and second task data structure;
generating a user interface with the identified difference; and
presenting the user interface on a computing device.
Claim 10:
receiving, using a processing unit, an indication that a current instance of an agricultural operation has begun in a geographic area;
during the current instance of the agricultural operation, receiving via an application programming interface, task characteristics of the agricultural operation;
generating a first task data structure that includes the task characteristics, a field identifier associated with the geographic area, and a task identifier;
storing the first task data structure in a database as associated with a first time period;
accessing a second task data structure for the agricultural operation associated with a second time period, the second time period being before the first time period;
inputting the first task data structure and second task data structure into a difference model; receiving an output from the difference model identifying a difference for a first characteristic of the task characteristics between first task data structure and second task data structure;
generating a user interface with the identified difference; and
presenting the user interface on a computing device.
Claim 19:
receiving, using a processing unit, an indication that a current instance of an agricultural operation has begun in a geographic area;
during the current instance of the agricultural operation, receiving via an application programming interface, task characteristics of the agricultural operation;
generating a first task data structure that includes the task characteristics, a field identifier associated with the geographic area, and a task identifier;
storing the first task data structure in a database as associated with a first time period;
accessing a second task data structure for the agricultural operation associated with a second time period, the second time period being before the first time period;
inputting the first task data structure and second task data structure into a difference model; receiving an output from the difference model identifying a difference for a first characteristic of the task characteristics between first task data structure and second task data structure;
generating a user interface with the identified difference; and
presenting the user interface on a computing device.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processing unit, storage device, application programming interface, database, difference model, user interface and computing device is recited at a high level of generality, i.e., as a generic computing and processing system. This c processing unit, storage device, application programming interface, database, difference model, user interface and computing device is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022] is directed to Step 2B. Therein, per Step 2B the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a processing unit, storage device, application programming interface, database, difference model, user interface and computing device. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processing unit, storage device, application programming interface, database, difference model, user interface and computing device type structure at paragraphs 0067: “Example computer system 500 includes at least one processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, processor cores, compute nodes, etc.), a main memory 504 and a static memory 506, which communicate with each other via a link 508. The computer system 500 may further include a video display unit 510, an input device 512 (e.g., a keyboard), and a user interface (UI) navigation device 514 (e.g., a mouse)..” Paragraph 0068: The storage device 516 includes a machine-readable medium 522 on which is stored one or more sets of data structures and instructions 524 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein.” Paragraph 0028: “the communication may occur using an application programming interface (API) such as API 116. An API provides a method for computing processes to exchange data. A web-based API (e.g., API 116) may permit communications between two or more computing devices such as a client and a server.” Paragraph 0034: “The specific storage layout and model used in by data store 118 may take several forms—indeed, a data store 118 may utilize multiple models. Data store 118 may be, but is not limited to, a relational database (e.g., SQL), non-relational database (NoSQL) a flat file database, object model, document details model, graph database, shared ledger (e.g., blockchain), or a file system hierarchy.” Paragraph 0049: “Difference model 122 may be more than one model in various examples. For example, a logistic regression model “ and paragraph 0022: “Client device 104 may be a computing device which may be, but is not limited to, a smartphone, tablet, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, set-top box, or another device that a user utilizes to communicate over a network. In various examples, a computing device includes a display module (not shown) to display information (e.g., in the form of specially configured user interfaces). In some embodiments, computing devices may comprise one or more of a touch screen, camera, keyboard, microphone, or Global Positioning System (GPS) device.” See also figure 5.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-9, 11-18 and 20 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2, 11 and 20 further limit the abstract idea by receiving a value of a task characteristic of the task characteristics of the agricultural operation; inputting the value of the task characteristic of the current instance into the difference model; receiving an output from the difference model identifying a recommendation for the agricultural operation (a more detailed abstract idea remains an abstract idea). Claims 3 and 12 further limit the abstract idea that the task characteristic is soil temperature, and the recommendation is a postponement recommendation based on the soil temperature being below a threshold value (a more detailed abstract idea remains an abstract idea). Claims 4 and 13 further limit the abstract idea that the task characteristics include real-time data collected from a piece of agricultural equipment performing the agricultural operation (a more detailed abstract idea remains an abstract idea). Claims 5 and 14 further limit the abstract idea that the task characteristics include user input data associated with the agricultural operation (a more detailed abstract idea remains an abstract idea). Claims 6 and 15 further limit the abstract idea that the task characteristics include an environmental sensor reading of the geographic area (a more detailed abstract idea remains an abstract idea). Claims 7 and 16 further limit the abstract idea by generating a field data structure data structure, the field data structure data structure including the first task data structure and second task data structure; and storing the field data structure in the database (a more detailed abstract idea remains an abstract idea). Claims 8 and 17 further limit the abstract idea that the user interface includes: a field selection portion configured to receive a selection of a field from a plurality of fields; a sub-selection portion configured to receive a selection of a sub-section of a field; and a difference portion displaying the identified difference (a more detailed abstract idea remains an abstract idea). And claims 9 and 18 further limit the abstract idea by receiving an identifier of a selected field from the field selection portion; receiving an identifier of a selected sub-section of the selected filed from the sub-selection portion; receiving an output from the difference model identifying a difference in a yield value for the selected sub-section in the first task data structure and second task data structure; and presenting the difference in the yield value in the user interface (a more detailed abstract idea remains an abstract idea).The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-11 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sauder et al., (US 2018/0146612 A1) hereinafter “Sauder”.
Claim 1:
Sauder as shown discloses a system, the system comprising:
a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: (¶ 0059: “Hardware/virtualization layer 150 comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system such as volatile or non-volatile memory, non-volatile storage,”);
receiving, using a processing unit, an indication that a current instance of an agricultural operation has begun in a geographic area (¶ 0121: “a user creates a trial live in real time while performing the farming operation. In one example, a user selects a record option from a device (e.g., display device in a machine, tablet device, etc.) to initiate a first region during a first operation (e.g., planting) and then selects a record or stop option at a later time to terminate an area defined by the first region. “);
during the current instance of the agricultural operation, (¶ 0121 ) receiving via an application programming interface, (¶ 0067: “ ingestion-sharing instructions 202 which are programmed to receive, translate, and ingest field data from third party systems via manual upload or APIs”)
task characteristics of the agricultural operation (¶ 0042: describes tasks characteristics of the agricultural operation: “Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), […]”);
generating a first task data structure (¶ 0051: “a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system” see also ¶ 0058: “ model and field data is stored in model and field data repository 160. […] Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data”);
that includes the task characteristics (¶ 0042: “ (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) fertilizer data (for example, nutrient type (Nitrogen, Phosphorous, Potassium), application type, application date, amount, source, method), (f) pesticide data (for example, pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, application date, amount, source, method), (g) irrigation data (for example, application date, amount, source, method),“)
a field identifier associated with the geographic area, and (¶ 0042: “(a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range)”);
a task identifier (¶ 0042, as explained above examples of field data includes various agricultural tasks identifiers e.g., harvesting, planting, etc.);
storing the first task data structure in a database (¶ 0051 describes the database) as associated with a first time period (¶ 0042: “harvest date […] planting date […] fertilizer data […] application date […] pesticide data […] application date […] irrigation data […] application date”);
accessing a second task data structure for the agricultural operation (¶ 0058: “ model and field data is stored in model and field data repository 160. […] Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data”, see also ¶ 0051);
associated with a second time period, the second time period being before the first time period (¶ 0042: “(b) harvest data (for example, […] Actual Production History (APH), […] and previous growing season information) and ¶ 0065: “ allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare.”);
inputting the first task data structure and second task data structure into a difference model (¶ 0065: “the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare” see also ¶ 0090: “the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both.”);
receiving an output from the difference model identifying a difference for a first characteristic of the task characteristics between first task data structure and second task data structure; generating a user interface with the identified difference; and presenting the user interface on a computing device (Figures 10 and 11, see also ¶ 0127-0129: “The comparison center user interface preferably includes an agronomic result (e.g., a yield value such as average yield in bushels per acre, economic yield in dollars per acre) corresponding to a plurality of criteria (e.g., seasons, fields, sub-field management zones, soil types, etc.).”);
Claims 10 and 19:
The limitations of claims 10 and 19 (¶0059) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above.
Claims 2, 11 and 20:
Sauder as shown discloses the following limitations:
receiving a value of a task characteristic of the task characteristics of the agricultural operation; inputting the value of the task characteristic of the current instance into the difference model; receiving an output from the difference model identifying a recommendation for the agricultural operation (¶ 0050: “The GUI may comprise controls for inputting data to be sent to agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.” see also ¶ 0090: “an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, and harvesting recommendations.”);
Claims 4 and 13:
Sauder as shown discloses the following limitations:
wherein the task characteristics include real-time data collected from a piece of agricultural equipment performing the agricultural operation (¶ 0044: “An agricultural apparatus 111 may have one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130.”);
Claims 5 and 14:
Sauder as shown discloses the following limitations:
wherein the task characteristics include user input data associated with the agricultural operation (¶ 0019: “to receive the communication from a device in response to at least one user input that is received in real time during a farming operation.” See also ¶ 0042: “(j) scouting observations (photos, videos, free form notes, voice recordings, voice transcriptions,”);
Claims 6 and 15:
Sauder as shown discloses the following limitations:
wherein the task characteristics include an environmental sensor reading of the geographic area (¶ 0082: “sensors 112 that may be used with seed planting equipment such as planters, drills, or air seeders include seed sensors, which may be optical, electromagnetic, or impact sensors; downforce sensors such as load pins, load cells, pressure sensors; soil property sensors such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors”);
Claims 7 and 16:
Sauder as shown discloses the following limitations:
generating a field data structure data structure, the field data structure data structure including the first task data structure and second task data structure; and storing the field data structure in the database (¶ 0051: “a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system” see also ¶ 0058: “model and field data is stored in model and field data repository 160. […] Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data”);.”);
Claims 8 and 17:
Sauder as shown discloses the following limitations:
wherein the user interface includes: a field selection portion configured to receive a selection of a field from a plurality of fields; a sub-selection portion configured to receive a selection of a sub-section of a field; and a difference portion displaying the identified difference (Figure 10, note a field selection portion, “Fields” note a sub-selection portion of a field “Homeplace” and note a difference portion displaying the identified difference “Difference” see also ¶ 0128: “A seasons option 510 can be selected for displaying seasonal comparison data or a fields option 512 can be selected for displaying fields comparison data. A field region 514 includes a selectable option (e.g., homeplace 520) for displaying comparison data for a particular farm or field”);
Claims 9 and 18:
Sauder as shown discloses the following limitations:
receiving an identifier of a selected field from the field selection portion; receiving an identifier of a selected sub-section of the selected filed from the sub-selection portion; receiving an output from the difference model identifying a difference in a yield value for the selected sub-section in the first task data structure and second task data structure; and presenting the difference in the yield value in the user interface (Figure 10, note a field selection portion, “Fields” note a sub-selection portion of a field “Homeplace” and note the output identifying a difference in yield value from Season A and Season B see also ¶ 0128: “A seasons option 510 can be selected for displaying seasonal comparison data or a fields option 512 can be selected for displaying fields comparison data. A field region 514 includes a selectable option (e.g., homeplace 520) for displaying comparison data for a particular farm or field”);
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 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sauder et al., (US 2018/0146612 A1) hereinafter “Sauder” as applied to claim 2 above, and further in view of Ethington et al., (US 2021/0350478 A1) hereinafter “Ethington”.
Claims 3 and 12:
Sauder as shown discloses the following limitations:
wherein the task characteristic is soil temperature, (Figure 10 illustrates “Planting Soil Temperature”);
Sauders as explained above recommendations for agricultural operations. Sauder is silent with regard of using a microservices. However, Ethington in an analogous art of agricultural management for the purpose of providing the following limitations as shown does:
and the recommendation is a postponement recommendation based on the soil temperature being below a threshold value (¶ 0088: “the planting advisor module recommends or excludes planting dates based on predicted workability. For example, dates at which a predicted planting-specific workability value is “Stop” may either be excluded or not recommended. In some examples, the planting advisor recommends or excludes planting dates based upon predicted weather events (e.g., temperature or precipitation). For example, planting dates may be recommended after which likelihood of freezing is lower than associated threshold values.”);
Both Sauder and Ethington teach agricultural management. Sauder teaches in the Abstract: “agricultural data analysis.” Ethington teaches in the Abstract “ recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ethington would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ethington to the teaching of Sauder would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the recommendation is a postponement recommendation based on the soil temperature being below a threshold value into similar systems. Further, as noted by Ethington “[a] technical effect of the systems and methods described herein include at least one of (a) improved utilization of agricultural fields through improved field condition monitoring; (b) improved selection of time and method of fertilization; (c) improved selection of time and method of pest control; (d) improved selection of seeds planted for the given location of soil; (e) improved field condition data for at a micro-local level; and (f) improved selection of time of harvest..” (Ethington ¶ 0122).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADJA CHONG whose telephone number is (571)270-3939. The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm 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, RUTAO WU can be reached on 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
Prosecution Insights