DETAILED ACTION
This action is written in response to the application filed 3/21/23. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. 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.
In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines, as well as guidance from MPEP § 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim … recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claim recites one or more limitations which—under their broadest reasonable interpretation—covers performance of the limitation in the mind (see table below).
Claim limitation
Examiner analysis
1. A computer-implemented method comprising: …
generating one or more device-related temporal predictions associated with the at least one device-related repair task by processing at least a portion of the obtained data using one or more artificial intelligence techniques; ….
This is a mental process akin to a human evaluation/judgment. Many AI techniques can be practically executed as mental processes, typically with the aid of pencil and paper, eg decision trees.
Because the claim recites limitations which can practically be implemented as mental processes, the claim recites a mental process.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the claim does not recite even generic computer hardware.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the additional limitations are addressed below:
obtaining data pertaining to one or more aspects of at least one device-related repair task;
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
performing one or more automated actions based at least in part on at least a portion of the one or more device-related temporal predictions;
This is insignificant post-solution activity: performing some non-specified action in response to the preceding analysis.
The only limitation on the performance of the described method is that it must be performed using a computer (“wherein the method is performed by at least one processing device comprising a processor coupled to a memory”). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The statement that the method is performed by computer does not satisfy the test of “inventive concept.” See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 134 S. Ct. 2347, 2360 (2014).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 12 and 17, which recite a computer-readable storage medium and an apparatus, respectively, as well as to dependent claims 2-11, 13-16 and 18-20. The additional limitations of the dependent claims are addressed briefly below. Taken alone, the additional elements of the dependent claims above do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim limitation
Examiner analysis
2. The computer-implemented method of claim 1, wherein generating one or more device-related temporal predictions comprises processing at least a portion of the obtained data using at least one neural network-based regressor.
This is a mental process akin to a human evaluation/judgment.
3. The computer-implemented method of claim 1, wherein generating one or more device-related temporal predictions comprises processing at least a portion of the obtained data using one or more decision tree-based ensemble machine learning algorithms.
This is merely additional information about one or more previously identified mental processes.
4. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically provisioning one or more resources in accordance with at least one of the one or more device-related temporal predictions.
This is insignificant post-solution activity: performing an unspecified allocation of unspecified resources based on the results of an unspecified preceding analysis step.
5. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically generating and outputting, to one or more entities associated with the at least one device-related repair task, one or more communications pertaining to the at least a portion of the one or more device-related temporal predictions.
This is a mental process akin to a human evaluation/judgment.
6. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on feedback related to the at least a portion of the one or more device-related temporal predictions.
This is a mental process akin to a human evaluation/judgment.
7. The computer-implemented method of claim 1, wherein generating one or more device-related temporal predictions comprises generating at least one prediction for a delivery timeline for at least one of a device and one or more parts thereof to at least one location associated with the at least one device-related repair task.
This is a mental process akin to a human evaluation/judgment.
8. The computer-implemented method of claim 1, wherein generating one or more device-related temporal predictions comprises generating at least one prediction for a manufacturing timeline for at least one of a device and one or more parts thereof in connection with the at least one device-related repair task.
This is a mental process akin to a human evaluation/judgment.
9. The computer-implemented method of claim 1, wherein generating one or more device-related temporal predictions comprises generating at least one prediction for one or more support service implementation timelines associated with the at least one device-related repair task.
This is a mental process akin to a human evaluation/judgment.
10. The computer-implemented method of claim 1, wherein obtaining data pertaining to one or more aspects of at least one device-related repair task comprises obtaining data pertaining to one or more of user information, device information, device part information, repair-related location information, device-related location information, repair task type, one or more temporal parameters associated with the at least one device-related repair task, and logistics provider information.
This is a mental process akin to a human evaluation/judgment.
11. The computer-implemented method of claim 1, further comprising:
training at least a portion of the one or more artificial intelligence techniques using multi- dimensional historical logistics-related data associated with one or more device-related repair tasks.
This is merely additional information about one or more previously identified mental processes.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2)the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bateman (US 2022/0300992 A1).
Regarding claims 1, 12 and 17, Bateman discloses a computer-implemented method (and a non-transitory processor-readable storage medium and apparatus) comprising:
obtaining data pertaining to one or more aspects of at least one device-related repair task;
The Examiner interprets “data pertaining to one or more aspects of at least one device-related repair task” according to its broadest reasonable interpretation as encompassing delivery data.
[0011] “As shown in FIGS. 1 and 2, an embodiment of a method for predicting transit time for a parcel associated with a user includes: retrieving historical delivery data (e.g., past delivery data) from a plurality of shipping carriers Silo.”
generating one or more device-related temporal predictions associated with the at least one device-related repair task by processing at least a portion of the obtained data using one or more artificial intelligence techniques; and
The Examiner interprets “temporal prediction” according to its broadest reasonable interpretation in view of the applicants specification as encompassing delivery timeline estimates. This interpretation is consistent with the illustrative descriptions in the Applicants specification, eg at p. 6, and dependent claim 7, (see excerpts below).
Applicant’s written description at p. 6: “Accordingly, at least one embodiment includes automatically generating logistics delivery timeline estimations using artificial intelligence techniques.”
Applicant’s dependent claim 7: “generating one or more device-related temporal predictions comprises generating at least one prediction for a delivery timeline for at least one of a device”.
[0011] “generating cross-carrier delivery features based on normalizing (e.g., standardizing) the historical delivery data across the plurality of shipping carriers S120; generating a cross-carrier delivery prediction model based on the cross-carrier delivery features S130;”
Fig. 3, reproduced below, “timeline of parcel 1 delivery”.
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performing one or more automated actions based at least in part on at least a portion of the one or more device-related temporal predictions;
[0011] “responding to the delivery estimate S170, which can additionally or alternatively include notifying a user with a delivery prediction notification S172, delivering the parcel based on the delivery estimate S174, and/or generating an automated system command S176, and/or any other suitable operations.” (Emphasis added.)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
[0066] processor.
Regarding independent claim 12, Bateman further discloses a non-transitory processor-readable storage medium ([0066] RAMs, ROMs, flash memory, hard drives, floppy drives).
Regarding independent claim 17, Bateman further discloses at least one processing device comprising a processor coupled to a memory ([0066] RAMs, ROMs, flash memory, hard drives, floppy drives).
Regarding claims 2, 13 and 18, Bateman discloses the further limitation wherein generating one or more device-related temporal predictions comprises processing at least a portion of the obtained data using at least one neural network-based regressor.
[0036] “back propagation neural networks”.
Regarding claims 3, 14 and 19, Bateman discloses the further limitation wherein generating one or more device-related temporal predictions comprises processing at least a portion of the obtained data using one or more decision tree-based ensemble machine learning algorithms.
[0036] decision trees, random forest.
Regarding claims 4, 15 and 20, Bateman discloses the further limitation wherein performing one or more automated actions comprises automatically provisioning one or more resources in accordance with at least one of the one or more device-related temporal predictions.
[0011] “responding to the delivery estimate S170, which can additionally or alternatively include notifying a user with a delivery prediction notification S172, delivering the parcel based on the delivery estimate S174, and/or generating an automated system command S176, and/or any other suitable operations.” (Emphasis added.)
[0016] “In another example, the technology can improve automated computing systems that rely on updated delivery information (e.g., factory inventory systems linked to production), where improved delivery estimates can lead to lower variance in operational conditions for decreasing computation time and resources required to allocate delivered physical resources.”
Regarding claims 5 and 16, Bateman discloses the further limitation wherein performing one or more automated actions comprises automatically generating and outputting, to one or more entities associated with the at least one device-related repair task, one or more communications pertaining to the at least a portion of the one or more device-related temporal predictions.
[0011] “responding to the delivery estimate S170, which can additionally or alternatively include notifying a user with a delivery prediction notification S172”. (Emphasis added.)
Regarding claim 6, Bateman discloses the further limitation wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on feedback related to the at least a portion of the one or more device-related temporal predictions.
[0036] “In examples, Block S130 and/or other portions of the method 100 can employ machine learning approaches including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), and any other suitable learning style.” (Emphasis added.)
Regarding claim 9, Bateman discloses the further limitation wherein generating one or more device-related temporal predictions comprises generating at least one prediction for one or more support service implementation timelines associated with the at least one device-related repair task.
See fig. 3 (reproduced supra) illustrating delivery timeline prediction for a plurality of parcels. (Ie ‘parcel 1’ and ‘parcel 2’.)
Regarding claim 10, Bateman discloses the further limitation wherein obtaining data pertaining to one or more aspects of at least one device-related repair task comprises obtaining data pertaining to one or more of user information, device information, device part information, repair-related location information, device-related location information, repair task type, one or more temporal parameters associated with the at least one device-related repair task, and logistics provider information.
[0030] “Regarding Block S120, determining delivery features is preferably based on processing delivery data according to one or more computer-implemented rules (e.g., a feature engineering rule, a user preference rule, etc.), but delivery features can be determined based on any suitable information. Block S120 preferably includes applying computer-implemented rules to process cross-carrier delivery data, but can additionally or alternatively include applying computer-implemented rules to process delivery data on a parcel-specific basis (e.g., generating features for packages above certain dimensions in a different manner than for packages below certain dimensions, where delivery features useful for tracking letter size packages may not be useful for tracking oversized and/or heavy packages, etc.), a shipping carrier-specific basis (e.g., generating features specific to UPS or USPS, etc.), a service level-specific basis (e.g., selecting a set of feature types tailored to UPS 2nd Day Air deliveries, etc.), a user-specific basis (e.g., selecting a set of feature types tailored to deliveries from a particular company, etc.), and/or on any suitable basis.” (Emphasis added.)
Regarding claim 11, Bateman discloses the further limitation comprising:
training at least a portion of the one or more artificial intelligence techniques using multi-dimensional historical logistics-related data associated with one or more device-related repair tasks.
[0031] “In a variation, Block S120 includes applying a feature engineering rule to select, filter, and/or otherwise generate features based on delivery data. In an example, the feature engineering rule can be a unit standardization rule, which can standardize a given type of delivery data that can vary in format across shipping carriers (e.g., a first shipping carrier providing parcel weight in pounds and a second shipping carrier providing parcel weight in ounces, etc.) for enabling meaningful comparisons between the delivery data (e.g., by converting all parcel weight data to ounces).”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The following are the references relied upon in the rejections below:
Bateman (US 2022/0300992 A1)
Cella (US 2019/0339686 A1)
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Bateman and Cella.
Regarding claim 7, Bateman discloses the further limitation wherein generating one or more device-related temporal predictions comprises generating at least one prediction for a delivery timeline for at least one of a device and one [other parcel] to at least one location associated with the at least one device-related repair task.
See fig. 3 (reproduced supra) illustrating delivery timeline prediction for a plurality of parcels. (Ie ‘parcel 1’ and ‘parcel 2’.)
Bateman does not disclose delivery prediction for a device and a component parcel thereof, ie for a delivery timeline for at least one of a device and one or more parts thereof. However, Cella discloses this feature.
[0068] “In embodiments, the industrial machine predictive maintenance system further comprises a service and delivery coordination facility that receives and processes information regarding services to perform on the industrial machine based on the at least one of orders and requests for service and parts. In embodiments, the service and delivery coordination facility validates the services to perform on the industrial machine while producing a ledger of service activity and results for the industrial machine.” (Emphasis added.)
[2247] “To facilitate at least semi-automated predictive maintenance, replacement parts, service, and the like may be automatically ordered based on a result of the predictive maintenance facility 5903 indicating that some form of preventive activity is required. An automatic part/service ordering facility 5913 may be connected directly or indirectly to the user interface/control facility 5909 to enable users to approve or adjust an automated order.” (Emphasis added.)
At the time of filing, it would have been obvious to a person of ordinary skill to monitor a plurality of related parcels (as taught by Cella) using the freight tracking prediction system of Bateman because repair work often necessitates having appropriate replacement components in stock before repair work can be completed (or often before it can begin). Combining these techniques would allow repair facility operators to optimize their workflow.
Regarding claim 8, Bateman discloses the further limitation wherein generating one or more device-related temporal predictions comprises generating at least one prediction for a … timeline for at least one of a device … in connection with the at least one device-related repair task.
See fig. 3 (reproduced supra) illustrating delivery timeline prediction for a plurality of parcels. (Ie ‘parcel 1’ and ‘parcel 2’.)
Bateman does not disclose prediction for a manufacturing processor for a device and a component parcel thereof, ie at least one prediction for a manufacturing timeline for at least one of a device and one or more parts thereof in connection with the at least one device-related repair task. However, Cella discloses this feature.
[0004] “Heavy industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results.” (Emphasis added.)
[1154] “In embodiments, a third party (e.g., RMOs, manufacturers) can aggregate data at the component level, equipment level, factory/installation level and provide a statistically valid data set against which to optimize their own systems.”
The obviousness analysis of claim 7 applies equally here.
Additional Relevant Prior Art
The following references were identified by the Examiner as being relevant to the disclosed invention, but are not relied upon in any particular prior art rejection:
Figlin discloses a task repository for managing and implementing pre-defined tasks, with applications to manufacturing and product distribution. (US 2013/0339254 A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vincent Gonzales whose telephone number is (571) 270-3837. The examiner can normally be reached on Monday-Friday 7 a.m. to 4 p.m. MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092.
Information regarding the status of an application may be obtained from the USPTO Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov.
/Vincent Gonzales/Primary Examiner, Art Unit 2124
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