DETAILED ACTION
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 .
Status of Claims
This action is in reply to the response filed 24 March 2026.
Claims 1, 3, 5, 12-13, and 15 have been amended.
Claims 17-19 have been added.
Claims 1-19 are pending and have been examined.
Response to Amendments and Remarks
Specification
The specification was objected to because of informalities. Applicant has amended the specification to overcome the objections to the specification. Accordingly, the objection to the specification has been withdrawn.
Claim Objections
Claim 13 was objected to because of informalities. Applicant has amended the claims to overcome or render moot each of the objections. Accordingly, the objection of claim 13 has been withdrawn.
Claim Rejections - 35 USC § 112
Claims 1-16 were rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The Applicant has amended the claims to overcome or render moot each of the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph,. Accordingly, the rejection of claims 1-16 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, has been withdrawn. However, the amendment has introduced a new indefiniteness issue addressed below.
Claim Rejections - 35 USC § 101
Claims 1-12 were rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Applicant’s arguments, see pages 10, filed 24 March 2026, with respect to the rejection(s) of claim(s) 1-12 under 35 U.S.C. 101 have been fully considered and are persuasive.
Claim Rejections - 35 USC § 103
Claims 1-7 were rejected under 35 U.S.C. § 103 as being unpatentable as obvious over U.S. Pub. No. 2014/031979 by Drew, in view of U.S. Pub. No. 2006/0104497 by Jespersen et al. and U.S. Pub. No. 2006/0067209 by Sheehan et al. Claims 3-7 and 8-12 were rejected under 35 U.S.C. § 103 as being unpatentable as obvious over the combination of Drew, Jespersen and Sheehan in view of U.S. Pub. No. 2020/0159220 by Hurd et al. Claims 13-16 were rejected under 35 U.S.C. § 103 as being unpatentable as obvious over Drew in view of Sheehan and Hurd.
Applicant’s arguments, see pages 11-13, filed 26 March 2026, with respect to the rejection(s) of claim(s) 1-7 under 35 U.S.C. § 103 as being unpatentable over U.S. Pub. No. 2014/031979 by Drew, in view of U.S. Pub. No. 2006/0104497 by Jespersen et al. and U.S. Pub. No. 2006/0067209 by Sheehan et al. have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made for claims 1-10 in view of U.S. Pub. No. 2014/031979 by Drew, in view of U.S. Pub. No. 2020/0159220 by Hurd et al. and further in view of U.S. Pub. No. 2006/0104497 by Jespersen et al. and U.S. Pub. No. 2006/0067209 by Sheehan et al.
Applicant argues that Drew discloses a system that:
uses a positioning module to store the position history of the vehicle and provide feedback to the operator. Drew does not teach any features to meet the claimed reusable digital asset with machine instructions. In contrast, Drew repeatedly references the operator of the vehicle who receives feedback through displays and other alerts.
Drew's map and route information identified in the office action do not include machine instructions for the working machine to execute as part of a mission plan. The map and route information may be displayed or used to provide alerts to the operator.
In the new ground(s) of rejection the examiner relies upon Hurd for performing the execution of the route following, however, maintains that the route corresponds to the reusable digital asset.
Applicant further argues:
Specifically, Drew does not teach (1) receiving a project request, (2) analyzing that request to determine a project operation and requirements for the project operation, and (3) determining that a reusable digital asset meets those requirements.
The examples identified in the office action do not teach the distinct elements required in this combination of steps: i.e. a project request, a project operation and a reusable digital asset. For example, the office action points to Drew para. [0052] and emphasizing that "the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer." It is unclear how this statement is applied to the claim language. An operator entering an identifier to retrieve route information does not appear to disclose a project request, determined project operation or reusable digital asset as claimed, let alone the three distinct elements.
The examiner respectfully disagrees. Drew teaches:
(1) request (See at least Drew wherein the project request is a route for a particular parcel or customer and wherein the minimum requirements may be a route that traverses the parcel or an best actual route, a composite route or optimum route to traverse the parcel as discussed below [0052] “…After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer.), See also [0056] …” In this regard, for example, the client application 522 may include software for enabling a respective one of the clients 520 to communicate with the network 530 for requesting and/or receiving information indicative of vehicle position and/or orientation in the context of a route or parcel in the form of a deliverable component (e.g., as downloadable software to configure the client, or as a transferable memory device including instructions to configure the client). As such, for example, the client application 522 may include corresponding executable instructions for configuring the client 520 to provide corresponding functionalities as described in greater detail herein.”,
(2) analyzing that request to determine a project operation and requirements for the project operation (See at least Drew wherein the project request is a route for a particular parcel or customer and wherein the minimum requirements may be a route that traverses the parcel or an best actual route, a composite route or optimum route to traverse the parcel route for completing the mowing or tilling of the selected parcel of land as discussed below. See for example [0021] The operator (or others) may therefore be enabled to recognize certain conditions that should be avoided, drive in accordance with a recommended or optimal route, provide proof of performance or execution, and/or the like.” See also [0048] “ In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria.” ); and
(3) determining that a reusable asset meets those requirements (See Drew which discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See Drew [0047-0049] “Examples of previous or derivative routes that may be presented include an optimal route (theoretical), a best route (actual), a last route, an average route, a composite route, a selected route, and/or the like. In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” See also [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future.” See also Drew [0058] “As such, for example, the application server 540 may include processing logic and memory enabling the application server 540 to access and/or execute stored computer readable instructions for performing various functions. In an example embodiment, one function that may be provided by the application server 540 may be the identification of, the recording of and/or receiving of information indicative of vehicle position and/or orientation in the context of a route or parcel. Alternatively or additionally, the function may include generating maps of parcels, merging position information recorded with geographic data from other sources (e.g., Google Earth, image data, topographical data, and/or the like)”).
Applicant argues with respect to the combination with Hurd:
Hurd also fails to disclose the combination of distinct features missing from Drew, Jespersen and Sheehan. Hurd does not disclose the development or use of a mission plan incorporating machine instructions from reusable digital asset and other operational instructions as claimed. Hurd teaches an integrated platform for autonomous agricultural equipment.
The examiner respectfully disagrees. As applied in the new grounds of rejection, Hurd is relied upon to executes a mission plan that includes in addition to the reusable data asset (a programmed route) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan (see at least Hurd [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0026] [0032] for autonomously controlling the vehicle based on the mission plan commands. See also [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking.)
The remaining arguments were found persuasive and are addressed by the new grounds of rejection below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: mission plan is configured in claim 13. The examiner can find no structural support for the mission plan. The examiner notes there is structural support from mission planning system.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Further, the examiner notes that claim 13-16 are system claims. ““[A]pparatus claims cover what a device is, not what a device does.” Hewlett-Packard Co.v.Bausch & Lomb Inc., 909 F.2d 1464, 1469, 15 USPQ2d 1525, 1528 (Fed. Cir. 1990) (emphasis in original). A claim containing a “recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus” if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987)” See MPEP 2114 (II) . For example, the examiner notes that the limitation regarding receiving the mission plan including machine instructions from a reusable digital asset and additional operation instructions, does not further limit the claim. An application gateway that is configured to receive instructions and is capable of receiving instructions, meets the claim as written.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 13-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding 13, the claim limitation “mission plan is configured to” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The examiner is unable to find the corresponding structure in the specification. Therefore, claims 13 is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph.
Claims 14-16 depend from claim 13 and are similarly rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, based on their dependency on claim 13.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 13, the claim limitation “mission plan configured to” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function of “keep prepared” in the claim. Therefore, claim 13 is indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 11-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites “ a machine learning model” in line 2. Claim 11 depends from claim 9, which recites “the machine learning model” and further depends from claim 8 which recites “a machine learning model”. It is not clear if the machine learning model of claim 11 is the same or different than that recited in claims 8 and 9. Further, claim 12 recites “the machine learning model”. It is not clear if the machine learning model referred to in claim 12 is referring back to the machine learning model of claim 11 or claims 8 and 9.
Claims 14-16 depend from claim 13 and are similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, based on their dependency on claim 13.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drew (US Pub. No. US-20140371979-A1, hereinafter “Drew”), in view of Hurd et al. (US Pub. No. 2020/0159220, hereinafter “Hurd”) in view of Jespersen et al. (US Pub. No. US-20060104497-A1, hereinafter “Jespersen") and Sheehan et al. (US Pub. No. US-20060067209-A1) .
Regarding claim 1, Drew discloses a management system for automated work, comprising:
a networked server system comprising (see at least Drew Figure 5 and [0055] “In this regard, FIG. 5 illustrates a system in which an embodiment of the present invention may be employed by processing of data at a central facility (e.g., application server 540). Thus, for example, although the apparatus of FIG. 2 may be embodied at a yard maintenance vehicle itself, in other embodiments such an apparatus may alternatively or additionally be located at a central facility or server.” See also [0032-0033] and Fig. 2 which discloses that the positioning module may be disposed in the riding yard maintenance vehicle and/or a remote computer, including at the central facility [0055]):
a server communication [[card]] configured to communicate over a communication network (see at least Drew Figure 5 and [0041-0042] “For example, in some cases, Bluetooth, WiFi or other wireless communication modules may be provided by the device interface 220 in order to allow wireless downloading of software, support information or other data, or allow wireless uploading of data to network devices for support, management or other purposes. In some embodiments, Bluetooth, WiFi or other short range wireless communication modules may be used to communicate data to an intermediate device (e.g., a cell phone), which may then communicate the data to a computer or other device at which certain analysis and/or display may be performed. In still other cases, a removable memory device may be used to transfer information from the memory 214 to the removable memory device and thereafter to the remote computer” See also [0057-0059] “In an example embodiment, one of the devices to which the clients 520 may be coupled via the network 530 may include one or more application servers (e.g., application server 540), and/or a database server 542, which together may form respective elements of a server network 532.” See also [0032-0033].[0033] The processing circuitry 210 may be configured to perform data processing, control function execution and/or other processing and management services according to an example embodiment of the present invention. In some embodiments, the processing circuitry 210 may be embodied as a chip or chip set. In other words, the processing circuitry 210 may comprise one or more physical packages (e.g., chips) including materials, components and/or wires on a structural assembly (e.g., a baseboard). The structural assembly may provide physical strength, conservation of size, and/or limitation of electrical interaction for component circuitry included thereon. The processing circuitry 210 may therefore, in some cases, be configured to implement an embodiment of the present invention on a single chip or as a single "system on a chip." As such, in some cases, a chip or chipset may constitute means for performing one or more operations for providing the functionalities described herein.”)
a server controller operatively connected to the server communication [[card]] and the server memory, the server controller is configured to manage the server memory drive and communication over the server communication [[card]] (see at least Drew Figure 5 and Figure 2 wherein the processing circuitry 210 including a processor and memory corresponds to the server controller and wherein of the positioning model 150 is part of the server as described in [0032]);
a working machine (See at least Drew Figures 1A and 1B and 0022] FIG. 1, which includes FIGS. 1A and 1B, illustrates an example of a riding yard maintenance vehicle 10 having a bagging attachment 12.), comprising:
[[an application gateway having a first communication card]] configured to communicate over the communication network with the server communication [[card]] (see at least Drew Figure 5, client 520 including client application 522, connected to network 530 which is connected to the server 532, 540 see also Figure 2. See also Drew [0055] “ As shown in FIG. 5, a system 500 according to an example embodiment may include one or more clients 520 that may, in some cases, be associated with different corresponding riding yard maintenance vehicles or other remote nodes… As such, each one of the clients 520 may be, for example, a computer (e.g., a personal computer, laptop computer, network access terminal, or the like) or may be another form of computing device (e.g., a personal digital assistant (PDA), cellular phone, smart phone, or the like) capable of communication with a network 530 via any short range (e.g., WiFi, Bluetooth, etc.) or long range communication protocols (e.g., 3G, 4G, LTE, etc.). As such, the clients 520 may be fixed at or integrated into riding yard maintenance vehicles, may be removable or transferable accessories or components associated with riding yard maintenance vehicles, or may be separate devices (e.g., a smart phone) capable of running applications useable in connection with operation of riding yard maintenance vehicles.”),
a machine control unit comprising a machine processor and a machine memory, which stores machine operating instructions (see at least Drew Figure 2, and processing circuitry 210 and [0034] “In an example embodiment, the processing circuitry 210 may include one or more instances of a processor 212 and memory 214 that may be in communication with or otherwise control a device interface 220 and, in some cases, a user interface 230.” and [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses” See also [0040] “As such, in some embodiments, the processor 212 (or the processing circuitry 210) may be said to cause each of the operations described in connection with the positioning module 150 by directing the positioning module 150 to undertake the corresponding functionalities responsive to execution of instructions or algorithms configuring the processor 212 (or processing circuitry 210) accordingly. As an example, the positioning module 150 may be configured to record position and/or orientation information, or other operational parameters regarding the tasks performed by riding yard maintenance vehicle 10 as described herein. The positioning module 150 may then, in some cases, process the information to generate alerts, warnings, route optimization, route guidance, maintenance recommendations, position histories, work histories, and/or the like.”), and
a machine actuator system having a machine control actuator (see at least Drew [0023] FIG. 1A illustrates a side view of the riding yard maintenance vehicle 10 and FIG. 1B illustrates a perspective view of the riding yard maintenance vehicle 10. The riding yard maintenance vehicle may include a steering assembly 20 (e.g., including a steering wheel, handle bars, or other steering apparatus) functionally connected to wheels of the riding yard maintenance vehicle 10 to which steering inputs are provided (e.g., the front and/or rear wheels in various different embodiments) to allow the operator to steer the riding yard maintenance vehicle 10. In some embodiments, the riding yard maintenance vehicle 10 may include seat 30 that may be disposed at a center, rear or front portion of the riding yard maintenance vehicle 10. The operator may sit on the seat 30, which may be disposed to the rear of the steering assembly 20 to provide input for steering of the riding yard maintenance vehicle 10 via the steering assembly 20.” [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses”); and
wherein the server controller receives a project request via the server communication [[card]] and analyzes the project request to determine a project operation and minimum requirements to complete the project operation (See at least Drew wherein the project request is a route for a particular parcel or customer and wherein the minimum requirements may be a route that traverses the parcel or an best actual route, a composite route or optimum route to traverse the parcel as discussed below [0052] “…After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer. Alerts, warnings, route optimization and other services, such as those described herein by way of example, may further be provided relative to self mapped route information.” See also [0056] In some cases, each one of the clients 520 may include (or otherwise have access to) memory for storing instructions or applications for the performance of various functions and a corresponding processor for executing stored instructions or applications. Each one of the clients 520 may also include software and/or corresponding hardware for enabling the performance of the respective functions of the clients as described herein in relation to recording (or providing information for remote recording) of information indicative of vehicle position and/or orientation. In an example embodiment, one or more of the clients 520 may include a client application 522 configured to operate in accordance with an example embodiment of the present invention. In this regard, for example, the client application 522 may include software for enabling a respective one of the clients 520 to communicate with the network 530 for requesting and/or receiving information indicative of vehicle position and/or orientation in the context of a route or parcel in the form of a deliverable component (e.g., as downloadable software to configure the client, or as a transferable memory device including instructions to configure the client). As such, for example, the client application 522 may include corresponding executable instructions for configuring the client 520 to provide corresponding functionalities as described in greater detail herein.” See also Drew determining minimum requirements [0021] The operator (or others) may therefore be enabled to recognize certain conditions that should be avoided, drive in accordance with a recommended or optimal route, provide proof of performance or execution, and/or the like.” See also [0048] “ In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria.” ); and
wherein the server controller analyzes reusable digital asset characteristics to identify a reusable digital asset having the reusable digital asset characteristics that meet the minimum requirement to complete the project operation and selects the reusable digital asset from the reusable digital asset storage and wherein the reusable digital asset comprises machine instructions; (The examiner notes that the instant application at [0039] indicates that reusable digital assets include maps, routes, settings, etc. and thus, the examiner uses the stored map and/or route information of Drew to teach the reusable digital assets. See Drew which discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See Drew [0047-0049] “Examples of previous or derivative routes that may be presented include an optimal route (theoretical), a best route (actual), a last route, an average route, a composite route, a selected route, and/or the like. In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” See also [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future.” See also Drew [0058] “As such, for example, the application server 540 may include processing logic and memory enabling the application server 540 to access and/or execute stored computer readable instructions for performing various functions. In an example embodiment, one function that may be provided by the application server 540 may be the identification of, the recording of and/or receiving of information indicative of vehicle position and/or orientation in the context of a route or parcel. Alternatively or additionally, the function may include generating maps of parcels, merging position information recorded with geographic data from other sources (e.g., Google Earth, image data, topographical data, and/or the like)”).
wherein a mission planning system of the networked server generates a mission plan for the working machine, wherein the mission plan includes machine instructions from the selected reusable digital asset and the mission plan includes additional operational instructions (see at least Drew, Figure 5, module of application server. Drew discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See at least See Drew [0047-0049] “Examples of previous or derivative routes that may be presented include an optimal route (theoretical), a best route (actual), a last route, an average route, a composite route, a selected route, and/or the like. In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future. “ See also [0064] In an example embodiment, a method for processing position information of a riding yard maintenance vehicle, as shown in FIG. 6, may include receiving information indicative of vehicle position and/or orientation at operation 600 and generating route information based on the position information to define a current route at operation 610. The method may further include comparing the current route to a previous route or a derivative route at operation 620 and providing feedback to the operator based on information associated with the current route at operation 630. The feedback may be guidance related, may be a visual representation of the current route and/or the derivative route, may include alerts or warnings, may include a map view, may include waypoints and data associated with the waypoints, or any of a number of other visual or audible feedback queues. In some cases, the method may include (in addition to or as an alternative to operation 620) generating a derivative route based on one or more routes associated with a same location as the current route at operation 640.” See also [0060] for additional instructions).
Drew teaches displaying the machine instructions from the mission plan, but does not teach wherein the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions. Further, while the examiner asserts that Drew teaches the mission plan includes additional operational instructions (a composite route derived from a previous route (i.e. a reusable asset) and additional operational instructions based on additional routes, obstacle data, third party data as discussed above), to further the prosecution the examiner relies upon Hurd to teach this element.
Hurd teaches the mission plan includes additional operational instructions and further teaches wherein the mission plan includes additional operation instructions the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions (see at least Hurd [0041-0043] The perception system 160 incorporates data processing techniques that perform object detection, classification, ranging, and trajectory analysis in one or more additional modules or software subsystems. This may include. …evaluate point-cloud data collected from LiDar systems to extract a ground plane and cluster and segment data representing objects in a field of view of the machinery and vehicles 102….[0042] The perception and safety system 160 may also include applying geo-referencing techniques to tag each object identified and evaluate GPS data to identify position and heading, for example, of machines 102 used in agricultural activities 104. This may also be used to “learn” distance, position, and identification of objects, and applied to train one or more artificial intelligence models, for example to improve identification of images obtained using thermal cameras, and improve depth relation…[0043] This may be also used for path planning manipulation of vehicle attributes such as braking, shifting, turning, and gear or mode selection, for example to calculate a vehicle's drivable pathway based on heading and position data, and the machine's operational characteristics such as turning radius capability.” See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0026] [0032] for autonomously controlling the vehicle based on the mission plan commands. See also [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Drew with the teaching of Hurd, because as Hurd teaches the common software structural architecture allows for collection of data that can be used to “learn” and improve on vehicle-to-vehicle functionality for future operation and further the system can fuse data from multiple sensors and vehicle to provide the autonomously-operated machinery with situational awareness to avoid obstacles and other terrain characteristics (see at least Hurd [0039-0040]).
The examiner notes that while the combination of Drew and Hurd teaches a server capable of communicating over a communication network and a working machine capable of communicating over the communication network with the server, the combination does not explicitly teach a server communication card, an application gateway, and a first communication card.
Jespersen teaches a server communication card (see at least [0076] “The server 220 includes a wireless communication card 234 for communicating with wireless portable devices 240. These devices 240 are similar to portable digital assistants (PDAs).”.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Drew and Hurd with the teaching of Jespersen to include a server communication card, with a reasonable expectation of success, because as Jespersen teaches a communication card allows for communication with wireless portable devices. (see at least Jespersen[0076]). Further Jespersen is analogous art, as servers are used in a variety of technologies, and further Jespersen indicates that the teachings are applicable to vehicles.
The combination of Drew, Hurd and Jespersen does not teach an application gateway, and a first communication card
Sheehan teaches an application gateway, and a first communication card (see at least Sheehan [0028-0029] “The application gateway architecture provides support for multi-vendor facilities architectures and is able to control supported apparatus by hosting, storing and communicating operating rules and protocols necessary to communicate with and manage any particular piece of facilities equipment. …[0029] The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (e.g., about the size of a deck of cards) or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications. The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications.”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Drew, Hurd and Jespersen to include an application gateway having a first communication card as taught by Sheehan, with a reasonable expectation of success, because as Sheehan teaches the application gateway with a communication card allows for connection, communication and management of equipment and devices (see at least Sheehan [0029-0029]).
Regarding claim 2, the combination of Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 1, wherein the additional operational instructions are selected from a second reusable digital asset from the reusable digital asset storage (See at least Drew which teaches the mission plan includes additional operational instructions (a composite route derived from a previous route (i.e. a reusable asset) and additional operational instructions based on additional routes (i.e. a second reusable digital asset). See Drew [0047-0049] and [0064] as cited above regarding the composite route from routes stored in memory. [0064] “In an example embodiment, a method for processing position information of a riding yard maintenance vehicle, as shown in FIG. 6, may include receiving information indicative of vehicle position and/or orientation at operation 600 and generating route information based on the position information to define a current route at operation 610. The method may further include comparing the current route to a previous route or a derivative route at operation 620 and providing feedback to the operator based on information associated with the current route at operation 630. The feedback may be guidance related, may be a visual representation of the current route and/or the derivative route, may include alerts or warnings, may include a map view, may include waypoints and data associated with the waypoints, or any of a number of other visual or audible feedback queues. In some cases, the method may include (in addition to or as an alternative to operation 620) generating a derivative route based on one or more routes associated with a same location as the current route at operation 640.”). See also Hurd see at least Hurd [0041-0043] The perception system 160 incorporates data processing techniques that perform object detection, classification, ranging, and trajectory analysis in one or more additional modules or software subsystems. This may include. …evaluate point-cloud data collected from LiDar systems to extract a ground plane and cluster and segment data representing objects in a field of view of the machinery and vehicles 102….[0042] The perception and safety system 160 may also include applying geo-referencing techniques to tag each object identified and evaluate GPS data to identify position and heading, for example, of machines 102 used in agricultural activities 104. This may also be used to “learn” distance, position, and identification of objects, and applied to train one or more artificial intelligence models, for example to improve identification of images obtained using thermal cameras, and improve depth relation…[0043] This may be also used for path planning manipulation of vehicle attributes such as braking, shifting, turning, and gear or mode selection, for example to calculate a vehicle's drivable pathway based on heading and position data, and the machine's operational characteristics such as turning radius capability.” See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan including the configuration characteristics controlled by the actuators [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0026] [0032] for autonomously controlling the vehicle based on the mission plan commands. See also [0043], [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking.).
Regarding claim 3, the combination of Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 1, wherein the mission plan includes operational rules to guide the working machine while the working machine implements the mission plan (See at least Drew Figure 3 and [0051-0053] wherein the tilt level, the tilt for a predetermined time, speed restrictions, and threshold distance of the route to be operational rules to guide the working machine's implementation of the mission plan. [0051-0053] “As an example, if a hill or ditch has a steep slope that would present a rollover risk if approached the wrong way (e.g., sideways instead of straight on), such areas may be identified as tilt exposure risk areas on a map view or other display showing route information. In some embodiments, the tilt exposure risk may also be used to trigger process interventions. For example, if tilt as indicated by the accelerometer 250 reaches a predefined level, the positioning module 150 may be configured to stop providing driving power to a blade motor that drives the cutting blade or blades within the cutting deck 40 or to a drive motor that provides power to the wheels. … Thus, the positioning module 150 may configured to track the amount of time that the riding yard maintenance vehicle 10 spends at a particular angle (or angles above a threshold) and issue a warning when the amount of time exceeds a predetermined amount. In some cases, speed restricted areas (e.g., based on slope or obstacle frequency) may also be identified to the operator as area with increased risk exposure …After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer. Alerts, warnings, route optimization and other services, such as those described herein by way of example, may further be provided relative to self mapped route information….[0053] The recordation of position and/or orientation data for the riding yard maintenance vehicle 10 may provide a number of uses to operators. As indicated above, route optimization services, route guidance services, and the provision of alerts and warnings may all be provided based on the storage of route information including position and/or orientation data. … ” See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also Hurd Figure 1 and 3 [0043] and [0046-0047] wherein the mission plan in addition to the route includes operational rules for the at least the AutoTill application 174 and the AutoPlant application including the turning radius, gears speed, and braking).
Regarding claim 4, the combination of Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 3, wherein the operational rules are selected from another reusable digital asset from the reusable digital asset storage (See Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also Hurd Figure 1 and 3 [0043] and [0046-0047] wherein the mission plan in addition to the route includes operational rules for the at least the AutoTill application 174 and the AutoPlant application including the turning radius, gears speed, and braking. The AutoTill and AutoPlant applications are reusable digital assets stored on the server. See also at least Drew Figure 3 and [0051-0053] and [0047] as cited above with respect to claim 3. The examiner interprets the tilt level, the tilt for a predetermined time, speed restrictions, and threshold distance of the route to be operational rules that are stored as another reusable digital asset).
Regarding claim 5, Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 1, wherein the mission plan includes trigger-based operational rules to modify the working operations of the working machine while the working machine implements the mission plan (See at least Hurd [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also Hurd Figure 1 and 3 [0043] and [0046-0047] wherein the mission plan in addition to the route includes operational rules for the AutoTill application 174 and the AutoPlant application including the turning radius, gears speed, and braking. See also Hurd [0038] wherein the operational rules can be selected by the trigger of user input. See also Hurd [0049] and [0052] “At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102.” See also [0055-0056] which teaches that the vehicle controls are manipulated based on sensor data stored of multiple vehicles and thus the sensor data and the machine learning model is reused. See also at least Drew Figure 3 and [0051-0053] and [0047] as cited above with respect to claim 3. The examiner interprets the tilt level, the tilt for a predetermined time, speed restrictions, and threshold distance of the route to be operational rules that are stored trigger based events that modify the operations of the vehicle).
Regarding claim 6, Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 5, wherein the trigger-based operational rules are selected from another reusable digital asset from the reusable digital asset storage (See at least Hurd [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also Hurd Figure 1 and 3 [0038], [0043] and [0046-0047] wherein the mission plan in addition to the route includes operational rules for the AutoTill application 174 and the AutoPlant application including the turning radius, gears speed, and braking and wherein the operational rules can be selected by the trigger of user input. The AutoTill and AutoPlant applications are reusable digital assets stored on the server. See also Hurd [0049] and [0052] “At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102.” See also [0055-0056] which teaches that the vehicle controls are manipulated based on sensor data from another reusable digital asset (the machine learning model)).
Regarding claim 7, Drew, Hurd, Jespersen and Sheehan teaches the management system according to claim 1, wherein the reusable digital asset from the reusable digital asset storage has been created from an analysis of a project plan by an analysis system, wherein the analysis identifies subsections of the project plan that are reusable for other project operations (The examiner notes that the instant application at [0039] indicates that reusable digital assets include maps, routes, settings, etc. and thus, the examiner interprets using portions of maps and routes of many past routes after analysis of the past routes and maps of Drew to teach the reusable digital assets. See at least Drew Figure 3 and 4 wherein portions or subsections of the route are reused 330 and obstacle locations from the map are reused to determine the optimal route after analysis. See at least Drew [0052] “…After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer. Alerts, warnings, route optimization and other services, such as those described herein by way of example, may further be provided relative to self mapped route information.” See also [0056] In some cases, each one of the clients 520 may include (or otherwise have access to) memory for storing instructions or applications for the performance of various functions and a corresponding processor for executing stored instructions or applications. Each one of the clients 520 may also include software and/or corresponding hardware for enabling the performance of the respective functions of the clients as described herein in relation to recording (or providing information for remote recording) of information indicative of vehicle position and/or orientation. In an example embodiment, one or more of the clients 520 may include a client application 522 configured to operate in accordance with an example embodiment of the present invention. In this regard, for example, the client application 522 may include software for enabling a respective one of the clients 520 to communicate with the network 530 for requesting and/or receiving information indicative of vehicle position and/or orientation in the context of a route or parcel in the form of a deliverable component (e.g., as downloadable software to configure the client, or as a transferable memory device including instructions to configure the client). As such, for example, the client application 522 may include corresponding executable instructions for configuring the client 520 to provide corresponding functionalities as described in greater detail herein.” See also [0047-0049] “In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” See also [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future.” See also Hurd Figure 1 and 3 [0038], [0043] and [0046-0047] wherein the AutoTill application 174 and the AutoPlant application are reusable digital assets stored on the server. See also Hurd [0049], [0052] [0055-0056]).
Regarding claim 8, the combination of Drew, Hurd, Jespersen, and Sheehan teaches the management system according to claim 1, wherein the reusable digital asset comprises a machine learning model (see at least Hurd Figure 1 and [0038], [0043] , [00149] [0046-0047] and [0052-0057] wherein [0057] discloses that the data processing modules 112 (which includes AutoTill application 174 and the AutoPlant application) can include artificial intelligence including neural networks See also Hurd [0052-0056] which teaches information from sensors of more than one vehicle is analyzed by a neural network (i.e. a model) specifically [0052] “At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” See also [0049] which teaches sensor information from more than one machine is used for the analysis.).
Regarding claim 9, the combination of Drew, Hurd, Jespersen, and Sheehan, teach the management system according to claim 8, wherein the machine learning model is configured for execution by the machine control unit to manage a sensor array system of the working machine (see at least Hurd Figure 1 and [0038], [0043] , [00149] [0046-0047] and [0052-0057] wherein [0057] discloses that the data processing modules 112 (which includes AutoTill application 174 and the AutoPlant application) can include artificial intelligence including neural networks. See also Hurd, [0052-0056] “[0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” See also [0049] which teaches sensor information from more than one machine is used for the analysis.)
Regarding claim 10, the combination of Drew, Hurd, Jespersen, and Sheehan teach the management system according to claim 1, wherein the working machine includes a sensor array system connected to the machine control unit and wherein the machine control unit applies the machine instructions based on the reusable digital asset to manage the sensor array system (see at least Drew Figure 2 and [0036-0037]), and Hurd, [0052-0056] which teaches information from sensors of more than one vehicle is analyzed by a neural network (i.e. a model) specifically [0052] “At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” See also [0049] which teaches sensor information from more than one machine is used for the analysis.).
Regarding claim 17, the combination of Drew, Hurd, Jespersen, and Sheehan teach the management system according to claim 1, wherein the minimum requirements include machine characteristics for the working machine (See Hurd wherein the instructions include f how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also Hurd Figure 1 and 3 [0043] and [0046-0047] wherein the mission plan in addition to the route includes operational rules for the at least the AutoTill application 174 and the AutoPlant application including the turning radius, gears speed, and braking. The AutoTill and AutoPlant applications are reusable digital assets stored on the server. See also at least Drew Figure 3 and [0051-0053] and [0047] as cited above with respect to claim 3. The examiner interprets the tilt level, the tilt for a predetermined time, speed restrictions, and threshold distance of the route to be operational rules that are stored).
Regarding claim 18, the combination of Drew, Hurd, Jespersen and Sheehan teach the management system according to claim 1, wherein the project operation is one of a plurality of operations identified by the server controller from the project request and the mission plan further comprises additional machine instructions from a plurality of additional reusable digital assets, and wherein the machine control unit executes the additional machine instructions, and the machine actuator system controls the working machine in response to the additional machine instructions (See at least Drew wherein the project operation is mowing or tilling a piece of land and the mission plan includes the route for a particular parcel or customer as cited above see Drew [0052] [0056]. See also Drew Figure 5, module of application server. Drew at [0047-0049] discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking. See also Hurd [0041-0043] and [0026] [0032] for executing the machine instructions based on the additional machine instructions.
Regarding claim 19, Drew discloses a management system for automated work, comprising:
a networked server system comprising (see at least Drew Figure 5 and [0055] “In this regard, FIG. 5 illustrates a system in which an embodiment of the present invention may be employed by processing of data at a central facility (e.g., application server 540). Thus, for example, although the apparatus of FIG. 2 may be embodied at a yard maintenance vehicle itself, in other embodiments such an apparatus may alternatively or additionally be located at a central facility or server.” See also [0032-0033] and Fig. 2 which discloses that the positioning module may be disposed in the riding yard maintenance vehicle and/or a remote computer, including at the central facility [0055]):
a server communication [[card]] configured to communicate over a communication network (see at least Drew Figure 5 and [0041-0042] “For example, in some cases, Bluetooth, WiFi or other wireless communication modules may be provided by the device interface 220 in order to allow wireless downloading of software, support information or other data, or allow wireless uploading of data to network devices for support, management or other purposes. In some embodiments, Bluetooth, WiFi or other short range wireless communication modules may be used to communicate data to an intermediate device (e.g., a cell phone), which may then communicate the data to a computer or other device at which certain analysis and/or display may be performed. In still other cases, a removable memory device may be used to transfer information from the memory 214 to the removable memory device and thereafter to the remote computer” See also [0057-0059] “In an example embodiment, one of the devices to which the clients 520 may be coupled via the network 530 may include one or more application servers (e.g., application server 540), and/or a database server 542, which together may form respective elements of a server network 532.” See also [0032-0033].[0033] The processing circuitry 210 may be configured to perform data processing, control function execution and/or other processing and management services according to an example embodiment of the present invention. In some embodiments, the processing circuitry 210 may be embodied as a chip or chip set. In other words, the processing circuitry 210 may comprise one or more physical packages (e.g., chips) including materials, components and/or wires on a structural assembly (e.g., a baseboard). The structural assembly may provide physical strength, conservation of size, and/or limitation of electrical interaction for component circuitry included thereon. The processing circuitry 210 may therefore, in some cases, be configured to implement an embodiment of the present invention on a single chip or as a single "system on a chip." As such, in some cases, a chip or chipset may constitute means for performing one or more operations for providing the functionalities described herein.”)
a server controller operatively connected to the server communication [[card]] and the server memory, the server controller is configured to manage the server memory drive and communication over the server communication [[card]] (see at least Drew Figure 5 and Figure 2 wherein the processing circuitry 210 including a processor and memory corresponds to the server controller and wherein of the positioning model 150 is part of the server as described in [0032]);
a working machine (See at least Drew Figures 1A and 1B and 0022] FIG. 1, which includes FIGS. 1A and 1B, illustrates an example of a riding yard maintenance vehicle 10 having a bagging attachment 12.), comprising:
[[an application gateway having a first communication card]] configured to communicate over the communication network with the server communication [[card]] (see at least Drew Figure 5, client 520 including client application 522, connected to network 530 which is connected to the server 532, 540 see also Figure 2. See also Drew [0055] “ As shown in FIG. 5, a system 500 according to an example embodiment may include one or more clients 520 that may, in some cases, be associated with different corresponding riding yard maintenance vehicles or other remote nodes… As such, each one of the clients 520 may be, for example, a computer (e.g., a personal computer, laptop computer, network access terminal, or the like) or may be another form of computing device (e.g., a personal digital assistant (PDA), cellular phone, smart phone, or the like) capable of communication with a network 530 via any short range (e.g., WiFi, Bluetooth, etc.) or long range communication protocols (e.g., 3G, 4G, LTE, etc.). As such, the clients 520 may be fixed at or integrated into riding yard maintenance vehicles, may be removable or transferable accessories or components associated with riding yard maintenance vehicles, or may be separate devices (e.g., a smart phone) capable of running applications useable in connection with operation of riding yard maintenance vehicles.”),
a machine control unit comprising a machine processor and a machine memory, which stores machine operating instructions (see at least Drew Figure 2, and processing circuitry 210 and [0034] “In an example embodiment, the processing circuitry 210 may include one or more instances of a processor 212 and memory 214 that may be in communication with or otherwise control a device interface 220 and, in some cases, a user interface 230.” and [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses” See also [0040] “As such, in some embodiments, the processor 212 (or the processing circuitry 210) may be said to cause each of the operations described in connection with the positioning module 150 by directing the positioning module 150 to undertake the corresponding functionalities responsive to execution of instructions or algorithms configuring the processor 212 (or processing circuitry 210) accordingly. As an example, the positioning module 150 may be configured to record position and/or orientation information, or other operational parameters regarding the tasks performed by riding yard maintenance vehicle 10 as described herein. The positioning module 150 may then, in some cases, process the information to generate alerts, warnings, route optimization, route guidance, maintenance recommendations, position histories, work histories, and/or the like.”), and
a machine actuator system having a machine control actuator (see at least Drew [0023] FIG. 1A illustrates a side view of the riding yard maintenance vehicle 10 and FIG. 1B illustrates a perspective view of the riding yard maintenance vehicle 10. The riding yard maintenance vehicle may include a steering assembly 20 (e.g., including a steering wheel, handle bars, or other steering apparatus) functionally connected to wheels of the riding yard maintenance vehicle 10 to which steering inputs are provided (e.g., the front and/or rear wheels in various different embodiments) to allow the operator to steer the riding yard maintenance vehicle 10. In some embodiments, the riding yard maintenance vehicle 10 may include seat 30 that may be disposed at a center, rear or front portion of the riding yard maintenance vehicle 10. The operator may sit on the seat 30, which may be disposed to the rear of the steering assembly 20 to provide input for steering of the riding yard maintenance vehicle 10 via the steering assembly 20.” [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses”); and
wherein the server controller receives a project request via the server communication [[card]] and analyzes the project request to determine a plurality of project operations and minimum requirements to complete each of the plurality of project operations, and wherein the plurality of project operations include a first project operation and [[a second project operation]] (See at least Drew wherein the project request is a route for a particular parcel or customer and wherein the minimum requirements may be a route that traverses the parcel or an best actual route, a composite route or optimum route to traverse the parcel as discussed below [0052] “…After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer. Alerts, warnings, route optimization and other services, such as those described herein by way of example, may further be provided relative to self mapped route information.” See also [0056] In some cases, each one of the clients 520 may include (or otherwise have access to) memory for storing instructions or applications for the performance of various functions and a corresponding processor for executing stored instructions or applications. Each one of the clients 520 may also include software and/or corresponding hardware for enabling the performance of the respective functions of the clients as described herein in relation to recording (or providing information for remote recording) of information indicative of vehicle position and/or orientation. In an example embodiment, one or more of the clients 520 may include a client application 522 configured to operate in accordance with an example embodiment of the present invention. In this regard, for example, the client application 522 may include software for enabling a respective one of the clients 520 to communicate with the network 530 for requesting and/or receiving information indicative of vehicle position and/or orientation in the context of a route or parcel in the form of a deliverable component (e.g., as downloadable software to configure the client, or as a transferable memory device including instructions to configure the client). As such, for example, the client application 522 may include corresponding executable instructions for configuring the client 520 to provide corresponding functionalities as described in greater detail herein.” See also Drew determining minimum requirements [0021] The operator (or others) may therefore be enabled to recognize certain conditions that should be avoided, drive in accordance with a recommended or optimal route, provide proof of performance or execution, and/or the like.” See also [0048] “ In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria.” ); and
wherein the server controller analyzes the reusable digital asset characteristics for each of the plurality of reusable digital assets to identify a first reusable digital asset having the reusable digital asset characteristics that meet the minimum requirements to complete the first project operation and [[a second reusable digital asset having the reusable digital asset characteristics that meet the minimum requirements to complete the second project operation]] (The examiner notes that the instant application at [0039] indicates that reusable digital assets include maps, routes, settings, etc. and thus, the examiner uses the stored map and/or route information of Drew to teach the reusable digital assets. See Drew which discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See Drew [0047-0049] “Examples of previous or derivative routes that may be presented include an optimal route (theoretical), a best route (actual), a last route, an average route, a composite route, a selected route, and/or the like. In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” See also [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future.” See also Drew [0058] “As such, for example, the application server 540 may include processing logic and memory enabling the application server 540 to access and/or execute stored computer readable instructions for performing various functions. In an example embodiment, one function that may be provided by the application server 540 may be the identification of, the recording of and/or receiving of information indicative of vehicle position and/or orientation in the context of a route or parcel. Alternatively or additionally, the function may include generating maps of parcels, merging position information recorded with geographic data from other sources (e.g., Google Earth, image data, topographical data, and/or the like)”).
wherein the server controller selects the first reusable digital asset and [[the second reusable digital asset from the reusable digital asset storage]];
wherein a mission planning system of the networked server generates a mission plan for the working machine, wherein the mission plan includes the executable machine instructions from the first reusable digital asset and [[the second reusable digital asset,]] and the mission plan includes additional operational instructions (see at least Drew, Figure 5, module of application server. Drew discloses determining a best route or a composite route based on stored routes. A composite route is a previous route (a reusable digital asset) with additional operational instructions. See at least See Drew [0047-0049] “Examples of previous or derivative routes that may be presented include an optimal route (theoretical), a best route (actual), a last route, an average route, a composite route, a selected route, and/or the like. In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future. “ See also [0064] In an example embodiment, a method for processing position information of a riding yard maintenance vehicle, as shown in FIG. 6, may include receiving information indicative of vehicle position and/or orientation at operation 600 and generating route information based on the position information to define a current route at operation 610. The method may further include comparing the current route to a previous route or a derivative route at operation 620 and providing feedback to the operator based on information associated with the current route at operation 630. The feedback may be guidance related, may be a visual representation of the current route and/or the derivative route, may include alerts or warnings, may include a map view, may include waypoints and data associated with the waypoints, or any of a number of other visual or audible feedback queues. In some cases, the method may include (in addition to or as an alternative to operation 620) generating a derivative route based on one or more routes associated with a same location as the current route at operation 640.” See also [0060] for additional instructions).
Drew teaches displaying the machine instructions from the mission plan, but does not teach wherein the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions. Further, while the examiner asserts that Drew teaches the mission plan includes additional operational instructions (a composite route derived from a previous route (i.e. a reusable asset) and additional operational instructions based on additional routes, obstacle data, third party data as discussed above), to further the prosecution the examiner relies upon Hurd to teach this element. Finally, Drew does not explicitly disclose the second reusable digital asset with minimum requirements to complete the second project operation, selecting the second reusable digital asset, and executing the second reusable digital asset as claimed.
Hurd teaches the mission plan includes additional operational instructions and further teaches wherein the mission plan includes additional operation instructions the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions (see at least Hurd [0041-0043] The perception system 160 incorporates data processing techniques that perform object detection, classification, ranging, and trajectory analysis in one or more additional modules or software subsystems. This may include. …evaluate point-cloud data collected from LiDar systems to extract a ground plane and cluster and segment data representing objects in a field of view of the machinery and vehicles 102….[0042] The perception and safety system 160 may also include applying geo-referencing techniques to tag each object identified and evaluate GPS data to identify position and heading, for example, of machines 102 used in agricultural activities 104. This may also be used to “learn” distance, position, and identification of objects, and applied to train one or more artificial intelligence models, for example to improve identification of images obtained using thermal cameras, and improve depth relation…[0043] This may be also used for path planning manipulation of vehicle attributes such as braking, shifting, turning, and gear or mode selection, for example to calculate a vehicle's drivable pathway based on heading and position data, and the machine's operational characteristics such as turning radius capability.” See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0026] [0032] for autonomously controlling the vehicle based on the mission plan commands. See also [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking.)
Finally, Hurd further teaches the second reusable digital asset with minimum requirements to complete the second project operation, selecting the second reusable digital asset, and executing the second reusable digital asset as claimed (see Hurd [0046] and [0047] for the second reusable asset and/or the additional instructions, as cited above the instructions are executed).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Drew with the teaching of Hurd, because as Hurd teaches the common software structural architecture allows for collection of data that can be used to “learn” and improve on vehicle-to-vehicle functionality for future operation and further the system can fuse data from multiple sensors and vehicle to provide the autonomously-operated machinery with situational awareness to avoid obstacles and other terrain characteristics (see at least Hurd [0039-0040]).
The examiner notes that while the combination of Drew and Hurd teaches a server capable of communicating over a communication network and a working machine capable of communicating over the communication network with the server, the combination does not explicitly teach a server communication card, an application gateway, and a first communication card.
Jespersen teaches a server communication card (see at least [0076] “The server 220 includes a wireless communication card 234 for communicating with wireless portable devices 240. These devices 240 are similar to portable digital assistants (PDAs).”.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Drew and Hurd with the teaching of Jespersen to include a server communication card, with a reasonable expectation of success, because as Jespersen teaches a communication card allows for communication with wireless portable devices. (see at least Jespersen[0076]). Further Jespersen is analogous art, as servers are used in a variety of technologies, and further Jespersen indicates that the teachings are applicable to vehicles.
The combination of Drew, Hurd and Jespersen does not teach an application gateway, and a first communication card
Sheehan teaches an application gateway, and a first communication card (see at least Sheehan [0028-0029] “The application gateway architecture provides support for multi-vendor facilities architectures and is able to control supported apparatus by hosting, storing and communicating operating rules and protocols necessary to communicate with and manage any particular piece of facilities equipment. …[0029] The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (e.g., about the size of a deck of cards) or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications. The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications.”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Drew, Hurd and Jespersen to include an application gateway having a first communication card as taught by Sheehan, with a reasonable expectation of success, because as Sheehan teaches the application gateway with a communication card allows for connection, communication and management of equipment and devices (see at least Sheehan [0029-0029]).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drew, Hurd, Jespersen, and Sheehan in further view of Anderson et al. (US 20220101280 A1, hereinafter “Anderson”).
Regarding claim 11, the combination of Drew, Hurd, Jespersen, Sheehan and Hurd teach the management system according to claim 9, wherein the reusable digital asset comprises a machine learning model that is incorporated into the mission plan, and the machine control unit executes the machine learning model (see at least Hurd, [0052-0056] “[0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” See also [0049] which teaches sensor information from more than one machine is used for the analysis.).
However, the combination does not explicitly teach that the machine learning model is executed to configure the sensor array system .
Anderson teaches that the machine learning model is executed to configure the sensor array system. (see at least Anderson [0189] The server 731, optionally a cloud server, can provide for detection of contamination using camera and multispectral camera sensors by reconfiguring the plurality of sensors for optimal visual recognition of the contamination 1225 based on an AI or machine learning algorithm. The AI can be trained to detect the presence of contamination by training a machine learning model to recognize images of the related contamination.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Drew, Hurd, Jespersen, and Sheehan with the teaching of Anderson, with a reasonable expectation of success, because as Anderson teaches this allows for the sensor to be free of contamination providing accurate data.
Regarding claim 12, the combination of Drew, Hurd, Jespersen, Sheehan, and Anderson teach the management system according to claim 10, wherein the machine control unit executes the machine learning model to analyze sensor data from the sensor array system (see at least Anderson [0189] The server 731, optionally a cloud server, can provide for detection of contamination using camera and multispectral camera sensors by reconfiguring the plurality of sensors for optimal visual recognition of the contamination 1225 based on an AI or machine learning algorithm. The AI can be trained to detect the presence of contamination by training a machine learning model to recognize images of the related contamination.” See also Hurd, [0052-0056] “[0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” .).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drew in view of Hurd and further in view of Sheehan.
Regarding claim 13, Drew discloses a working machine for automated work (See at least Drew Figures 1A and 1B and 0022] FIG. 1, which includes FIGS. 1A and 1B, illustrates an example of a riding yard maintenance vehicle 10 having a bagging attachment 12.), comprising:
[[an application gateway having a first communication card]] configured to communicate over a communication network with a networked server,(see at least Drew Figure 5, client 520 including client application 522, connected to network 530 which is connected to the server 532, 540 see also Figure 2. See also Drew [0055] “ As shown in FIG. 5, a system 500 according to an example embodiment may include one or more clients 520 that may, in some cases, be associated with different corresponding riding yard maintenance vehicles or other remote nodes… As such, each one of the clients 520 may be, for example, a computer (e.g., a personal computer, laptop computer, network access terminal, or the like) or may be another form of computing device (e.g., a personal digital assistant (PDA), cellular phone, smart phone, or the like) capable of communication with a network 530 via any short range (e.g., WiFi, Bluetooth, etc.) or long range communication protocols (e.g., 3G, 4G, LTE, etc.). As such, the clients 520 may be fixed at or integrated into riding yard maintenance vehicles, may be removable or transferable accessories or components associated with riding yard maintenance vehicles, or may be separate devices (e.g., a smart phone) capable of running applications useable in connection with operation of riding yard maintenance vehicles.” The examiner notes that the claim is directed to the working machine. The server, the server communication card and the server memory including reusable digital asset storage are not part of the working machine. For purposes of examination, the examiner will interpret the application gateway to be configured to communicate over a network with a server to meet the claim limitations).
a machine control unit comprising a machine processor and a machine memory which stores machine operating instructions; (see at least Drew Figure 2, and processing circuitry 210 including processor 212 and memory 214 See at least [0034] “In an example embodiment, the processing circuitry 210 may include one or more instances of a processor 212 and memory 214 that may be in communication with or otherwise control a device interface 220 and, in some cases, a user interface 230.” and [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses” See also [0040] “As such, in some embodiments, the processor 212 (or the processing circuitry 210) may be said to cause each of the operations described in connection with the positioning module 150 by directing the positioning module 150 to undertake the corresponding functionalities responsive to execution of instructions or algorithms configuring the processor 212 (or processing circuitry 210) accordingly. As an example, the positioning module 150 may be configured to record position and/or orientation information, or other operational parameters regarding the tasks performed by riding yard maintenance vehicle 10 as described herein. The positioning module 150 may then, in some cases, process the information to generate alerts, warnings, route optimization, route guidance, maintenance recommendations, position histories, work histories, and/or the like.”),and
a machine actuator system having a drive control actuator (see at least Drew [0023] FIG. 1A illustrates a side view of the riding yard maintenance vehicle 10 and FIG. 1B illustrates a perspective view of the riding yard maintenance vehicle 10. The riding yard maintenance vehicle may include a steering assembly 20 (e.g., including a steering wheel, handle bars, or other steering apparatus) functionally connected to wheels of the riding yard maintenance vehicle 10 to which steering inputs are provided (e.g., the front and/or rear wheels in various different embodiments) to allow the operator to steer the riding yard maintenance vehicle 10. In some embodiments, the riding yard maintenance vehicle 10 may include seat 30 that may be disposed at a center, rear or front portion of the riding yard maintenance vehicle 10. The operator may sit on the seat 30, which may be disposed to the rear of the steering assembly 20 to provide input for steering of the riding yard maintenance vehicle 10 via the steering assembly 20.” [0036] “ The device interface 220 may include one or more interface mechanisms for enabling communication with other devices (e.g., sensors of a sensor network and/or other accessories or functional units 270 such as motors, servos, switches or other operational control devices for automatic responses” See also “[0051] “For example, if tilt as indicated by the accelerometer 250 reaches a predefined level, the positioning module 150 may be configured to stop providing driving power to a blade motor that drives the cutting blade or blades within the cutting deck 40 or to a drive motor that provides power to the wheels.”); and
wherein [[the application gateway]] receives a mission plan from the server over the communication network, wherein the mission plan is configured to instruct the working machine to perform a project, and the mission plan includes machine instructions from a reusable digital and additional operational instructions (The examiner notes the claim interpretation provided above, Further, the examiner notes that the instant application at [0039] indicates that reusable digital assets include maps, routes, settings, etc. and thus, the examiner uses the map and/or route information of Drew to teach the reusable digital assets. However, the examiner notes that any data that is stored can be considered a reusable digital asset. See at least Drew [0052] “…After working of the parcel is complete, the route information may be saved in connection with the corresponding parcel or customer as one instance of a route run on the corresponding parcel. Thereafter, the operator may be enabled to enter the identifier and retrieve route information for the corresponding parcel or customer. Alerts, warnings, route optimization and other services, such as those described herein by way of example, may further be provided relative to self mapped route information.” See also [0056] “In this regard, for example, the client application 522 may include software for enabling a respective one of the clients 520 to communicate with the network 530 for requesting and/or receiving information indicative of vehicle position and/or orientation in the context of a route or parcel in the form of a deliverable component (e.g., as downloadable software to configure the client, or as a transferable memory device including instructions to configure the client). As such, for example, the client application 522 may include corresponding executable instructions for configuring the client 520 to provide corresponding functionalities as described in greater detail herein.” See also [0047-0049] “In some embodiments, the positioning module 150 may be configured to generate an optimal route for display to guide the operator or to inform the operator of a proposed path for working a parcel of land. The optimal route may be generated using algorithms designed to consider desirable criteria relative to performance over past routes, or relative to expected performance for a route for which detailed geographic information regarding a parcel is known a priori. As an example, the optimal route may be generated based on analyzing past routes for the time taken to complete each route and determining, based on the past routes, an optimal route for fastest completion of working a particular parcel. Alternatively, fuel economy, cut quality, blade torque minimization, risk of damage to operator or machine, tilt exposure minimization, or any number of other criteria may be used as criteria for generation of optimal routes. In some cases, the criteria used may be considered relative to a number or previous routes, or relative to models created based on vehicle performance testing, in order to generate recommendations regarding routes and/or specific driving strategies to be employed while running routes in order to optimally run the route relative to the selected criteria….[0049] FIG. 4 illustrates an example of an optimal route 330 that may be proposed relative to any criteria based on a previously run route (e.g., the route 300 of FIG. 3 or modeled performance). In FIG. 4 also, the parcel may be graphically displayed based on locally or remotely generated data, or based on third party data received in connection with the parcel. Moreover, in some cases, locally or remotely generated data may be merged with third party data to generate a view of the parcel with route information displayed thereon.” See also [0061] “ As such, the environment of FIG. 5 illustrates an example in which provision of recorded obstacle locations to an onsite device (e.g., the riding yard maintenance vehicle 10 or a mobile phone or device of the operator of the riding yard maintenance vehicle 10) may be accomplished by a remote entity (e.g., the application server 540). As such, the onsite device may be enabled to generate or utilize map or other geographic data even if the onsite device did not necessarily record some of the data itself. Moreover, in some embodiments, provision of location information may be provided from the onsite device to the remote entity for recording and later provision of such data, or of functions driven off of such data, either to the onsite device or another device that ends up at the same site in the future.” )
While Drew teaches receiving the mission plan (optimal route, route information and map including elevation and obstacles) and controls the working machine in response to the machine instructions (see at least Drew [0031] which teaches displaying based on the optimized route), Drew does not explicitly teach wherein the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions.
Hurd teaches wherein the machine control unit executes the machine instructions from the mission plan, and the machine actuator system controls the working machine in response to the machine instructions (see at least Hurd [0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102.” And [0056] “The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.”). Further Hurd teaches receiving additional operational instructions as cited above with respect to claim 1 (see at least Hurd [0041-0043] The perception system 160 incorporates data processing techniques that perform object detection, classification, ranging, and trajectory analysis in one or more additional modules or software subsystems. This may include. …evaluate point-cloud data collected from LiDar systems to extract a ground plane and cluster and segment data representing objects in a field of view of the machinery and vehicles 102….[0042] The perception and safety system 160 may also include applying geo-referencing techniques to tag each object identified and evaluate GPS data to identify position and heading, for example, of machines 102 used in agricultural activities 104. This may also be used to “learn” distance, position, and identification of objects, and applied to train one or more artificial intelligence models, for example to improve identification of images obtained using thermal cameras, and improve depth relation…[0043] This may be also used for path planning manipulation of vehicle attributes such as braking, shifting, turning, and gear or mode selection, for example to calculate a vehicle's drivable pathway based on heading and position data, and the machine's operational characteristics such as turning radius capability.” See also Hurd wherein in addition to the route (reusable data asset) the additional operation instructions of how to operate the functions and at what time are provided in the mission plan [0022] “The “stack” enables creation of a series of waypoints, which are sets of geographical coordinates used to determine a route and determine how to get from a first point to a second point (or multiple points) within such a route, as well as how to operate (what functions to perform, and at what time) while going along such a route.” See also [0026] [0032] for autonomously controlling the vehicle based on the mission plan commands. See also [0046] and [0047] for configuring characteristics including turning radius, gears, speed, braking.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Drew with the teaching of Hurd, with a reasonable expectation of success, because as Hurd teaches, this allows the working vehicle to avoid obstacles and increase safety (see at least Hurd [0056]).
The combination of Drew and Hurd do not explicitly teach an application gateway.
Sheehan teaches application gateway (see at least Sheehan [0028-0029] “The application gateway architecture provides support for multi-vendor facilities architectures and is able to control supported apparatus by hosting, storing and communicating operating rules and protocols necessary to communicate with and manage any particular piece of facilities equipment. …[0029] The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (e.g., about the size of a deck of cards) or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications. The application gateway may expose data from one or more devices 112A-C to a variety of enterprise applications 108, simultaneously, in standard formats recognizable to those applications. The application gateway, deployed on discreet serial processors 114 (or embedded communication cards 116, can connect the intelligent device(s) 112A-C to the enterprise application 108, or other upstream applications.”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Drew and Hurd to include an application gateway as taught by Sheehan, with a reasonable expectation of success, because as Sheehan teaches the application gateway with a communication card allows for connection, communication and management of equipment and devices (see at least Sheehan [0029-0029]).
Regarding claim 14, the combination of Drew, Hurd and Sheehan teach the working machine according to claim 13, further comprising a sensor array system connected to the machine control unit (see at least Hurd Figure 1 and [0040] “This module, referred to in FIG. 1 as a “perception” system 160, recognizes and distinguishes terrain to be covered by autonomously-operated equipment, and performs tasks such as identification of obstacles and other characteristics that enable safe, efficient, and confident performance of machines and vehicles 102 in such an operating environment.”)
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drew, Hurd, and Sheehan and further in view of Anderson et al. US 20220101280 A1, hereinafter “Anderson”).
Regarding claim 15, the combination of Drew, Hurd and Sheehan teach the working machine according to claim 14, wherein the reusable digital asset comprises a machine learning model that is included in the mission plan, and the machine control unit executes the machine learning model (see at least Hurd, [0052-0056] “[0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” See also [0049] which teaches sensor information from more than one machine is used for the analysis.).
However the combination does not teach that the machine learning model is executed to reconfigure the sensor array system to operate in a tailored configuration for the project.
Anderson teaches that a machine learning model is executed to reconfigure the sensor array system to operate in a tailored configuration for the project (see at least Anderson [0189] The server 731, optionally a cloud server, can provide for detection of contamination using camera and multispectral camera sensors by reconfiguring the plurality of sensors for optimal visual recognition of the contamination 1225 based on an AI or machine learning algorithm. The AI can be trained to detect the presence of contamination by training a machine learning model to recognize images of the related contamination.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Drew, Hurd and Sheehan with the teaching of Anderson, with a reasonable expectation of success because as Anderson teaches this allows for the sensor to be free of contamination providing accurate data.
Regarding claim 16, the combination of Drew, Hurd, Sheehan, and Anderson teach the working machine according to claim 15, wherein the machine control unit executes the machine learning model to analyze sensor data from the sensor array system (see at least Anderson [0189] “ The server 731, optionally a cloud server, can provide for detection of contamination using camera and multispectral camera sensors by reconfiguring the plurality of sensors for optimal visual recognition of the contamination 1225 based on an AI or machine learning algorithm. The AI can be trained to detect the presence of contamination by training a machine learning model to recognize images of the related contamination.” See also Hurd, [0052-0056] “[0052] At steps 460 and 470, the perception and safety system 160 analyzes sensor data collected from the plurality of sensors to detect and classify objects in field of view of the one or more machines 102, and determines a response to a presence of an object in that field of view. This may include initiating a change in navigational or other characteristics of navigational control of the one or more machines 102, such as for example changing speed, changing gears, stopping, braking, or adjusting some other aspect of the vehicular state of the one or more machines 102….[0055] “As noted above, the common software structural architecture 100 may allow for one or more layers or techniques of artificial intelligence to be applied to assist various aspects of the present invention to operate, such as for example analyzing images and reflected signals from the plurality of sensors to detect and classify the objects in the field of view of the one or more machines 102. Additionally, these artificial intelligence techniques may be used to evaluate a vehicular state for controlling the movement and the speed of the one or more machines 102 in response to the presence of objects, such as evaluating one or more of latitude, longitude, speed, heading, yaw-rate, a turning radius, and global position system zones representing a geographical location…[0056] Artificial intelligence and other types of machine learning may be used to associate and compare information in various types of sensor data, and to identify attributes in such sensor data, to produce detections of objects and to predict movement of those detected objects. The applications of artificial intelligence in the present invention may include one or more neural networks configured to develop relationships among and between the information within the various types of sensor data to recognize objects across images and reflected signals from different types of sensors having different fields of view, which are used to determine whether action needs to be taken to manipulate and control the autonomously-operated machines and vehicles 102. Artificial intelligence may therefore be used in the present invention at least within the perception and safety module 160 for safe operation of the one or more machines 102 in the performance of an agricultural activity 104.” .).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER M. ANDA whose telephone number is (571)272-5042. The examiner can normally be reached Monday-Friday 8:30 am-5pm MST.
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/JENNIFER M ANDA/Primary Examiner, Art Unit 3662
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