DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
2. Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
3. The abstract of the disclosure is objected to because the phrase "11483369.doc" on line 15 does not seem to be part of the abstract, nor is it related to any portion of the specification or part of any explanation of the claimed invention. Examiner recommends to remove that line from the abstract. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
4. 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.
5. 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.
6. Claim 18 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 18 recites the limitation "the inspection points" in line 1. There is insufficient antecedent basis for this limitation in the claim, as nowhere in the claim or in its parent claim 12 is the limitation “inspection points” defined. For examination purposes, claim 18 will be treated as a child of claim 17, as claim 17 does define “inspection points”.
Claim Rejections - 35 USC § 101
7. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
8. Claims 19-20 are rejected under 35 U.S.C. 101 because claim 19 recites: “A computer-readable storage device”. However, the ordinary meaning of a computer-readable storage device known in the art covers forms of non-transitory storage (CD-ROM, hard drives, etc.) and transitory storage (propagating signals, etc.). Therefore claim 19 is not statutory for reciting a computer readable storage device which covers both non-statutory subject matter and statutory subject matter. However, claim 19 may be amended to narrow the claim to cover only statutory embodiments by amending the claim to recite “A non-transitory computer readable storage device that stores…”. Claims that recite nothing but the physical characteristics of a form of energy, such as a frequency, voltage, or the strength of a magnetic field, define energy or magnetism, per se, and as such are non-statutory natural phenomena. O’Reilly, 56 U.S. (15 How.) at 112-14. Moreover, it does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in § 101. First, a claimed signal is clearly not a “process” under § 101 because it is not a series of steps. The other three § 101 classes of machine, compositions of matter and manufactures "relate to structural entities and can be grouped as ‘product’ claims in order to contrast them with process claims." 1 D. Chisum, Patents § 1.02 (1994). The three product classes have traditionally required physical structure or material.
Claim Rejections - 35 USC § 103
9. 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.
10. 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.
11. Claims 1-4, 6-7, 12-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20220198642 A1), hereinafter Sharma, in view of Finley (US 20240169601 A1), hereinafter Finley.
Regarding claim 1, Sharma teaches a method performed by one or more computers, the method comprising: receiving, from a database, input data corresponding to an agricultural machine (Fig. 2, paragraph 37, detection system obtaining data from sensor records for an autonomous farming vehicle, which is interpreted as receiving input data from a database for an agricultural machine); determining, from the input data, a plurality of attributes that each is associated with a respective characteristic or component of the agricultural machine (Fig. 3, paragraph 39, 45-47, wherein detection system obtains data from plurality of sensors and cameras to determine a plurality of attributes such as tire track marks, positioning, plugging, temperature, of tilling sweeps and shanks, which are defined as components of the agricultural machine); and determining, by a machine learning model, an evaluation of the agricultural machine by applying a set of parameters of the machine learning model on the plurality of attributes (Fig. 3, paragraph 58, wherein using machine learning and sensor data to determine whether there is a malfunction is interpreted as determining an evaluation of the agricultural machine; paragraph 60, wherein the malfunction detection is based on the machine-learned model’s output and parameters).
Sharma does not teach wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the agricultural machine; and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the agricultural machine.
Finley teaches wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the machine (paragraph 38, applying a machine learning model to sensor data to predict possible repairs for vehicle components interpreted as evaluating a prediction for respective repairs of components, including for farming vehicles); and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the machine (Fig. 4, paragraph 81, displaying augmented reality display identifying components to be manipulated to repair a vehicle interpreted as an extended reality display having user interface indicators representing performing a respective predicted repair).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sharma with the teachings of Finley for this invention of an extended reality display for evaluating repairs for an agricultural machine using machine learning. Sharma discusses using machine learning for detecting potential malfunctions and malfunctioning components for farming vehicles, in order to improve the detection rate and overall performance of the machines. Similarly, Finley discusses using machine learning to determine components in machinery such as appliances or vehicles that are in need of repair, and provides an augmented reality display of said components, to easily provide users with methods on how to repair or handle those components. While Finley does not specifically mention farming equipment, it would be obvious to extend its identification of vehicle components to agricultural machinery and vehicles such as tractors or combines, or to the farming vehicles discussed in Sharma. As both references discuss ways to using machine learning to identify components in machinery in need of repairs, it would be obvious to combine these references.
Regarding claim 2. Sharma in view of Finley discloses the method of claim 1. Additionally, Finley teaches the method of claim 1, wherein the extended reality representation indicates step-by-step instructions for performing the respective predicted repair or maintenance (Fig. 4, paragraph 81, wherein the augmented reality overlay gives instructions in multiple steps for repairing a component until the repair is complete, which is interpreted as step-by-step instructions).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 3, Sharma in view of Finley discloses the method of claim 1. Additionally, Sharma teaches the method of claim 1, wherein the method further comprises training the machine learning model (Fig. 3, paragraph 50, machine learning model is trained); and transforming a plurality of different training data formats to a format that is acceptable for the machine learning model ()paragraph 54, wherein sensor data can be preprocessed via digital signal processing, which is interpreted as transforming a plurality of data, including training data, into a format more acceptable for the machine learning model; paragraph 51, wherein machine learning model may be trained on data from sensor records).
Regarding claim 4, Sharma in view of Finley discloses the method of claim 1. Additionally, Sharma teaches the method of claim 1, wherein the plurality of attributes include one or more of hours of use, quantity of acreage harvested, make, model (paragraph 30, the monitoring system may evaluate data aggregated from farming vehicles, where the evaluated data includes the farming equipment used, which is interpreted as including the make and model of the equipment), repair history, work order history, quantity of bushels harvested, variety of crop, and geographical region of use (paragraph 30, the monitoring system may evaluate data aggregated from farming vehicles, where the evaluated data includes the farming environment such as crops grown and climate, which is interpreted as indicating crop variety and geographical region of use).
Regarding claim 6, Sharma in view of Finley discloses the method of claim 1. Additionally, Sharma teaches the method of claim 1, wherein the machine learning model comprises at least one of a neural network, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, a random forest model, a genetic algorithm, a Bayesian model, a Gaussian mixture model, a gradient boosting model, or a dimensionality reduction model (paragraph 63, wherein the machine learning model may use various machine learning techniques including neural networks, Bayesian model,, linear support vector machine, decision trees, boosted trees, or any suitable supervised or unsupervised model).
Regarding claim 7, Sharma in view of Finley discloses the method of claim 1. Additionally, Sharma teaches the method of claim 1, wherein the machine learning model has been trained using a plurality of sets of known attributes that correspond to a plurality of known agricultural machines (paragraph 52, wherein the machine learning model is trained based on data obtained from one or more farming equipment types, which is interpreted as corresponding to a plurality of known agricultural machines).
Regarding claim 12, Sharma teaches a non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon (paragraph 124-125) which, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: receiving, from a database, input data corresponding to an agricultural machine (Fig. 2, paragraph 37, detection system obtaining data from sensor records for an autonomous farming vehicle, which is interpreted as receiving input data from a database for an agricultural machine); determining, from the input data, a plurality of attributes that each is associated with a respective characteristic or component of the agricultural machine (Fig. 3, paragraph 39, 45-47, wherein detection system obtains data from plurality of sensors and cameras to determine a plurality of attributes such as tire track marks, positioning, plugging, temperature, of tilling sweeps and shanks, which are defined as components of the agricultural machine); and determining, by a machine learning model, an evaluation of the agricultural machine by applying a set of parameters of the machine learning model on the plurality of attributes (Fig. 3, paragraph 58, wherein using machine learning and sensor data to determine whether there is a malfunction is interpreted as determining an evaluation of the agricultural machine; paragraph 60, wherein the malfunction detection is based on the machine-learned model’s output and parameters).
Sharma does not teach wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the agricultural machine; and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the agricultural machine.
Finley teaches wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the machine (paragraph 38, applying a machine learning model to sensor data to predict possible repairs for vehicle components interpreted as evaluating a prediction for respective repairs of components, including for farming vehicles); and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the machine (Fig. 4, paragraph 81, displaying augmented reality display identifying components to be manipulated to repair a vehicle interpreted as an extended reality display having user interface indicators representing performing a respective predicted repair).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 13, Sharma in view of Finley discloses the medium of claim 12. Additionally, Finley teaches the medium of claim 12, wherein the extended reality representation indicates step-by-step instructions for performing the respective predicted repair or maintenance (Fig. 4, paragraph 81, wherein the augmented reality overlay gives instructions in multiple steps for repairing a component until the repair is complete, which is interpreted as step-by-step instructions).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 14, Sharma in view of Finley discloses the medium of claim 12. Additionally, Sharma teaches the medium of claim 12, wherein the method further comprises training the machine learning model (Fig. 3, paragraph 50, machine learning model is trained); and transforming a plurality of different training data formats to a format that is acceptable for the machine learning model (paragraph 54, wherein sensor data can be preprocessed via digital signal processing, which is interpreted as transforming a plurality of data, including training data, into a format more acceptable for the machine learning model; paragraph 51, wherein machine learning model may be trained on data from sensor records).
Regarding claim 15, Sharma in view of Finley discloses the medium of claim 12. Additionally, Sharma teaches the medium of claim 12, wherein the plurality of attributes include one or more of hours of use, quantity of acreage harvested, make, model (paragraph 30, the monitoring system may evaluate data aggregated from farming vehicles, where the evaluated data includes the farming equipment used, which is interpreted as including the make and model of the equipment), repair history, work order history, quantity of bushels harvested, variety of crop, and geographical region of use (paragraph 30, the monitoring system may evaluate data aggregated from farming vehicles, where the evaluated data includes the farming environment such as crops grown and climate, which is interpreted as indicating crop variety and geographical region of use).
Regarding claim 19, Sharma teaches a system, comprising: a computing device; and a computer-readable storage medium coupled to one or more processors and having instructions stored thereon (paragraph 124-125) which, when executed by the computing device, causes the computing device to perform operations, the operations comprising: receiving, from a database, input data corresponding to an agricultural machine (Fig. 2, paragraph 37, detection system obtaining data from sensor records for an autonomous farming vehicle, which is interpreted as receiving input data from a database for an agricultural machine); determining, from the input data, a plurality of attributes that each is associated with a respective characteristic or component of the agricultural machine (Fig. 3, paragraph 39, 45-47, wherein detection system obtains data from plurality of sensors and cameras to determine a plurality of attributes such as tire track marks, positioning, plugging, temperature, of tilling sweeps and shanks, which are defined as components of the agricultural machine); and determining, by a machine learning model, an evaluation of the agricultural machine by applying a set of parameters of the machine learning model on the plurality of attributes (Fig. 3, paragraph 58, wherein using machine learning and sensor data to determine whether there is a malfunction is interpreted as determining an evaluation of the agricultural machine; paragraph 60, wherein the malfunction detection is based on the machine-learned model’s output and parameters).
Sharma does not teach wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the agricultural machine; and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the agricultural machine.
Finley teaches wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the machine (paragraph 38, applying a machine learning model to sensor data to predict possible repairs for vehicle components interpreted as evaluating a prediction for respective repairs of components, including for farming vehicles); and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the machine (Fig. 4, paragraph 81, displaying augmented reality display identifying components to be manipulated to repair a vehicle interpreted as an extended reality display having user interface indicators representing performing a respective predicted repair).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 20, Sharma in view of Finley discloses the system of claim 19. Additionally, Finley teaches the system of claim 19, wherein the extended reality representation indicates step-by-step instructions for performing the respective predicted repair or maintenance (Fig. 4, paragraph 81, wherein the augmented reality overlay gives instructions in multiple steps for repairing a component until the repair is complete, which is interpreted as step-by-step instructions).
The motivation to combine would be the same as that set forth for claim 1.
12. Claims 5, 11, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Finley as applied to claims 1, 12 above, and further in view of Marlow (US 12056965 B1), hereinafter Marlow.
Regarding claim 5, Sharma in view of Finley discloses the method of claim 1. Additionally, Marlow teaches the method of claim 1, wherein the predicted repair or maintenance of the one or more components of the agricultural machine is based on: one or more attributes selected from hours of use, make, and model (Col. 12 line 58 – Col. 13 line 5, wherein the diagnostic platform uses machine learning to predict if components need to be replaced or not based on the specific type of vehicle, which is interpreted as being based on the make or model, and interpreted as including farming vehicles; Col. 9 lines 39-51, wherein the diagnostic platform may determine make or model of the vehicle); and one or more attributes selected from repair history, quantity of bushels harvested, variety of crop, and geographical region of use (Col. 12 line 66 – Col. 13 line 5, wherein the diagnostic platform uses machine learning to predict if components need to be replaced or not based on corresponding historical circumstances, which is interpreted as repair history).
It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Sharma in view of Finley to incorporate the teachings of Marlow for this method of predicting repairs for agricultural machinery. Sharma discusses using machine learning for detecting potential malfunctions and malfunctioning components for farming vehicles, in order to improve the detection rate and overall performance of the machines. Similarly, Finley discusses using machine learning to determine components in machinery such as appliances or vehicles that are in need of repair, and provides an augmented reality display of said components, to easily provide users with methods on how to repair or handle those components. Additionally, Marlow also teaches using machine learning to determine malfunctioning components that need replacing within vehicles, and displaying an augmented reality view of the vehicle to give a more clear and visible view of the malfunctioning components. Both Finley and Marlow discuss analogous art for using augmented reality to display malfunction components in vehicles. While neither Finley nor Marlow explicitly discuss farming equipment, it would be obvious to one in the art to extend their techniques of identifying components in vehicles to farming equipment and vehicles such as tractors or combines, or to the farming vehicles discussed in Sharma. As all three references discuss using machine learning to identify malfunctioning components in machinery, it would be obvious to combine them.
Regarding claim 11, Sharma in view of Finley disclose the method of claim 1. Additionally, Marlow teaches the method of claim 1, wherein the displaying the extended reality representation of the evaluation output to a user interface comprises a representation of a portion of the agricultural machine that is obscured (Fig. 5, Col. 11, lines 32-51, wherein the user device may display an augmented reality view depicting a portion of the vehicle that is not visible but would be visible if an exterior portion of the vehicle was displaced, which is interpreted as a portion of the machine that is obscured).
The motivation to combine would be the same as that set forth for claim 5.
Regarding claim 16, Sharma in view of Finley discloses the medium of claim 12. Additionally, Marlow teaches the medium of claim 12, wherein the predicted repair or maintenance of the one or more components of the agricultural machine is based on: one or more attributes selected from hours of use, make, and model (Col. 12 line 58 – Col. 13 line 5, wherein the diagnostic platform uses machine learning to predict if components need to be replaced or not based on the specific type of vehicle, which is interpreted as being based on the make or model, and interpreted as including farming vehicles; Col. 9 lines 39-51, wherein the diagnostic platform may determine make or model of the vehicle); and one or more attributes selected from repair history, quantity of bushels harvested, variety of crop, and geographical region of use (Col. 12 line 66 – Col. 13 line 5, wherein the diagnostic platform uses machine learning to predict if components need to be replaced or not based on corresponding historical circumstances, which is interpreted as repair history).
The motivation to combine would be the same as that set forth for claim 12.
13. Claims 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Finley as applied to claims 1, 12 above, and further in view of Dagley (US 20210342790 A1), hereinafter Dagley.
Regarding claim 9, Sharma in view of Finley discloses the method of claim 1. Additionally, Dagley teaches the method of claim 1, further comprising generating an evaluation output that comprises one or more of a creation of inspection points on the agricultural machine, a recommendation to run the agricultural machine, a recommendation to replace the agricultural machine, a recommendation to repair the agricultural machine (paragraph 38, determining a set of parts that should be repaired or replaced interpreted as recommending the replace or repair the machine), a prediction of failure probabilities of components on the agricultural machine, a list of parts tailored to the attributes of the agricultural machine (Fig. 6, paragraph 92, using machine learning model to recommend replacement parts for replacing a given part interpreted as providing a list of parts tailored to the attributes of the machine), or guidance to support resources, wherein the extended reality representation includes a representation of the evaluation output (Fig. 1C, paragraph 42, wherein extended reality device displays output allowing users to schedule a delivery or service appointment for replacing or repairing a part, which is interpreted as guidance for support resources).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sharma in view of Finley with the teachings of Dagley for this method of predicting repairs or replacements for components of agricultural machinery. Sharma discusses using machine learning for detecting potential malfunctions and malfunctioning components for farming vehicles, in order to improve the detection rate and overall performance of the machines. Similarly, Finley discusses using machine learning to determine components in machinery such as appliances or vehicles that are in need of repair, and provides an augmented reality display of said components, to easily provide users with methods on how to repair or handle those components. Likewise, Dagley discusses using machine learning to identify components of a vehicle in need of repair or replacement, and providing an extended reality display of the vehicle that allows users to easily find support to repair or replace those components. Both Finley and Dagley discuss having an augmented reality display of malfunctioning components of a vehicle, to allow users to more easily allow users to replace or repair those components. While neither Finley nor Dagley explicitly mentions farming machinery, it would be obvious to one of ordinary skill in the art to extend their techniques for identifying malfunctioning parts in vehicles to agricultural farming vehicles such as tractors or combines, or to the farming vehicles discussed in Sharma. As all three references discuss using machine learning to identify malfunctioning components in machinery, it would be obvious to combine them.
Regarding claim 17, Sharma in view of Finley discloses the medium of claim 12. Additionally, Dagley teaches the medium of claim 12, further comprising generating an evaluation output that comprises one or more of a creation of inspection points on the agricultural machine, a recommendation to run the agricultural machine, a recommendation to replace the agricultural machine, a recommendation to repair the agricultural machine (paragraph 38, determining a set of parts that should be repaired or replaced interpreted as recommending the replace or repair the machine), a prediction of failure probabilities of components on the agricultural machine, a list of parts tailored to the attributes of the agricultural machine (Fig. 6, paragraph 92, using machine learning model to recommend replacement parts for replacing a given part interpreted as providing a list of parts tailored to the attributes of the machine), or guidance to support resources, wherein the extended reality representation includes a representation of the evaluation output (Fig. 1C, paragraph 42, wherein extended reality device displays output allowing users to schedule a delivery or service appointment for replacing or repairing a part, which is interpreted as guidance for support resources).
The motivation to combine would be the same as that set forth for claim 9.
14. Claims 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Finley and Dagley as applied to claims 9, 17 above, and further in view of Marlow.
Regarding claim 10, Sharma in view of Finley and Dagley disclose the method of claim 9. Additionally, Marlow teaches the method of claim 9, wherein the inspection points can include information about a probability that a particular component will fail (Fig. 6, Col. 14, lines 13-31, wherein generating a highlighted x-ray interface highlighting potentially damaged vehicle components is interpreted as creating inspection points on the machine; Col. 14, lines 32-40, wherein components may be highlighted based on the likelihood they need to be repaired, which suggests that the inspection points includes information about a probability that a particular component will fail by indicating the likelihood the component needs to be replaced).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sharma in view of Finley with the teachings of Dagley for this method of predicting repairs or replacements for components of agricultural machinery. Sharma discusses using machine learning for detecting potential malfunctions and malfunctioning components for farming vehicles, in order to improve the detection rate and overall performance of the machines. Similarly, Finley discusses using machine learning to determine components in machinery such as appliances or vehicles that are in need of repair, and provides an augmented reality display of said components, to easily provide users with methods on how to repair or handle those components. Likewise, Dagley discusses using machine learning to identify components of a vehicle in need of repair or replacement, and providing an extended reality display of the vehicle that allows users to easily find support to repair or replace those components. Additionally, Marlow also teaches using machine learning to determine malfunctioning components that need replacing within vehicles, and displaying an augmented reality view of the vehicle to give a more clear and visible view of the malfunctioning components that may otherwise be obscured from view. Finley, Dagley, and Marlow each discuss analogous art for displaying an augmented reality view of a vehicle, in order to let users better repair or replace malfunctioning components. While neither of those three references explicitly discuss agricultural machinery, it would be obvious to one of ordinary skill to extend their techniques of identifying malfunction components in vehicles to agricultural machinery vehicles like tractors or combines, or any of the farming vehicles discussed in Sharma. As all four references discuss using machine learning to identify malfunctioning components in machinery, it would be obvious to combine them.
Regarding claim 18, Sharma in view of Finley and Dagley disclose the medium of claim 17. Additionally, Marlow teaches the medium of claim 17, wherein the inspection points can include information about a probability that a particular component will fail (Fig. 6, Col. 14, lines 13-31, wherein generating a highlighted x-ray interface highlighting potentially damaged vehicle components is interpreted as creating inspection points on the machine; Col. 14, lines 32-40, wherein components may be highlighted based on the likelihood they need to be repaired, which suggests that the inspection points includes information about a probability that a particular component will fail by indicating the likelihood the component needs to be replaced).
The motivation to combine would be the same as that set forth for claim 10.
Allowable Subject Matter
15. Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN W YICK whose telephone number is (571)272-4063. The examiner can normally be reached M-F 8-5.
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/JORDAN WAN YICK/Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612