DETAILED ACTION
Notice of 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 the Application
Claims 1-20 were pending and were rejected in the previous office action. Claims 1, 4-5, 7-9, 11, 14-16, 18, and 20 were amended. Claim 10 was canceled, and new claim 21 was added. Claims 1-9 and 11-21 remain pending and are examined in this office action.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/517,278, filed August 2, 2023.
Response to Arguments
Claim Objections:
Claims 1, 8, 18, and 20 were previously objected to. Claims 1, 8, 18 and 20 were amended to the address the previous issues, and the previous objections are withdrawn. However, see the new objection to claim 21.
35 USC § 101:
Applicant’s arguments regarding the § 101 rejection of claims 1-20 (pgs. 9-13, remarks filed 12/19/2025) have been fully considered, but they are not persuasive. It is noted that claim 10 is canceled and new claim 21 is added.
Step 2A Prong One: Applicant argues that “The Office Action at Pages 4-6 identifies several limitations that allegedly fall under the ‘certain methods of organizing human activity’ and/or ‘mental process’ groupings of abstract ideas. Applicant respectfully disagrees, and notes that while the claims may ‘involve’ one or more of the alleged abstract ideas, these are not ‘recited’ in the claims” (pg. 10, remarks). However, the examiner respectfully disagrees. As identified in the previous office action, and updated below, the claims are do not just “involve” an abstract idea – they clearly recite a number of limitations that describe both “certain methods of organizing human activity” and “mental processes” (e.g. “generate workload rankings,” “collect…current service data,” “receive…a query requesting service,” “adjust the workload rankings,” “determine the one or more repair facilities,” “display (i.e. output) a list of the one or more determined repair facilities”) and thus the claims contain limitations that recite an abstract idea.
Applicant further identifies that the machine-learning model and use of a user computer to display information are additional elements (also see pg. 10, remarks), and the examiner agrees. However, these elements are considered at Step 2A Prong Two/Step 2B, where they are generically recited computer elements being used to carry out the underlying abstract idea and/or add generic computer implementation after the fact to the claims, as is seen in the further analysis at Step 2A Prong Two and Step 2B below. They do not change that the claims recite an abstract idea at Step 2A Prong One.
Step 2A Prong Two: Applicant argues that “With the recitations described above as additional elements, the claims provide a specific, technical improvement over prior systems in the technical field of model-based ranking, resulting in improved computer systems that operate responsive to user queries to provide "real-time" or on-demand adjustments, thereby limiting the computing resources consumed by processing "batches" of collected data only when requested… Applicant's specification at paragraph [0137], for example, describes the benefits of improved computer functionality provided by the claimed invention, and these technical benefits are recited in the present claims, as amended. Indeed, each of the clauses of Claim 1 cited above forms part of a combination that integrates any alleged abstract idea into a practical application of computing technology. For example, the claimed combinations provide the practical application of improved, responsive machine-learning applications with batches of collected data. For at least these reasons, independent Claims 1, 18, and 20 are not "directed to" an abstract idea, and the Section 101 rejection of the claims should be withdrawn” (pgs. 10-12).
However, the examiner respectfully disagrees. First, the claims do not improve the purported field of “model-based ranking” – as is clear in the claims, this does not describe to a technical field, but instead describes the performance of ranking service providers (commercial process) on computers. “Real-time” adjustments describe nothing more than the speed or efficiency inherent to applying business processes on computers, which does not render an abstract idea eligible. Paragraph ¶ 0137 simply describes batch processing to reduce resource usage at a high level of generality, and does not describe some improvement to how computers would perform batch processing operations. Not only is this element not recited in the claims, but the concept of batch processing (i.e. grouping items together to consolidate work) and any resulting reduction, when described at a high level of generality, would at most be an improvement to the underlying abstract idea functions being carried out on computers (e.g. performing the collecting of data only when requested by a user). Any reduction is resources used is not provided by some technological improvement in the way a computer processes data, but by programming a computer to perform the abstract functions for collecting data and ranking service providers only when requested by a user. Neither the claims or the specification described a specific technical mechanism through which an improvement in the functioning of a computer (e.g. an improvement to the way a computer processor performs batch processing) is recited. In addition, the claims merely use a trained machine learning model to generate an output (adjust workload rankings) based on input data (historical service data, current service data, predicted current service capacity). These limitations do not recite an improvement to machine learning technology and/or the recited machine learning model, improve the functioning of computers, or otherwise improve any other technology. Instead, they use a trained machine learning recited at a high level of generality to carry out the abstract idea of adjusting/generating a workload ranking according to current and historical workload data for service providers. Furthermore, displaying or outputting information on an interactive user interface of a display simply uses a generic computer display in its ordinary capacity to display or output information in a user interface. This does not add anything more than use of generic computer implementation to apply the abstract idea.
Therefore, the examiner maintains that current claims 1-9 and 11-21 do not integrate the abstract idea into a practical application.
Step 2B: Applicant generally argues that claims 1-9 and 11-21 are eligible for the same reasons as described with respect to Step 2A Prong Two above. However, the examiner respectfully disagrees. As described above, nothing in the claims provides an improvement to machine learning technology, improves the functions of any of the recited computer elements themselves, or improves any other specific field of technology. Using existing technology as a tool to apply an abstract idea (e.g. “apply it”) does not provide an inventive concept or otherwise amount to significantly more than the abstract idea. No combination of the additional elements in the claims provide an improvement to technology or solve a technological problem using a technical solution.
Therefore, the § 101 rejection is maintained over current claims 1-9 and 11-21, and updated below based on the current amendments. Please see the current § 101 rejection of claims 1-9 and 11-21 below.
35 USC § 103:
Applicant’s arguments regarding the previous § 103 rejections of claims 1-20 (pgs. 13-15, remarks filed 12/19/2025) have been considered but they are moot, as they do not apply to the grounds of rejection applied in the current § 103 rejections below in response to applicant’s amendments.
Please see the current § 103 rejections of claims 1-9 and 11-21 below.
Claim Objections
Claim 21 is objected to because of the following informalities:
Claim 21 recites “wherein the plurality of current service data includes a reduced number of variables then the plurality of historical service data” – however, the examiner suggests amending this limitation to recite “wherein the plurality of current service data includes fewer variables than the plurality of historical service data”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 11-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-9, 11-17, and 21 recite “A computer system…” (i.e. a machine); claims 18-19 recite “A computer-implemented method…” (i.e. a process); and claim 20 recites “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon…” (i.e. an article of manufacture). These claims fall under one of the four categories of statutory subject matter and as a result, pass Step 1 of the subject matter eligibility test. However, “Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection.” See MPEP 2106.04. Accordingly, the examiner continues the subject matter eligibility analysis below.
Step 2A Prong One:
Independent claims 1, 18 and 20 recite limitations (additional elements omitted) for monitoring of a workload for a plurality of repair facilities (claims 1/18) and for detecting and acting upon operator reliance to vehicle alerts (claim 20), including limitations to:
generate workload rankings for a plurality of repair facilities…
collect, from the plurality of repair facilities, a plurality of current service data for the plurality of repair facilities;
receive…a query requesting service for repairing a user vehicle provided by one or more of the repair facilities;
based on at least in part upon content of the query…predict service capacity of the plurality of repair facilities, with input of the plurality of current service data to adjust the workload rankings for the plurality of repair facilities based upon a predicted current service capacity of the plurality of repair facilities…
determine the one or more repair facilities having availability based upon the adjusted workload rankings of the plurality of repair facilities to provide the service for repairing the user vehicle; and
display…one or more determined repair facilities
The limitations of independent claims 1, 18 and 20 above are determined to recite an abstract idea (ranking a plurality of repair facilities based on current service data, receiving a repair request to repair a vehicle, determining an adjusted ranking based upon current service data and a predicted current service capacity of the plurality of repair facilities, and determining and displaying one or more available repair facilities to service the repair request) for the reasons discussed in the following continued Step 2A Prong One analysis. Note that “An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016).
As described in MPEP 2106.04(a)(2)(II), claim limitations which recite commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) fall into the “certain methods of organizing human activity” category of judicial exceptions. Therefore, since the processes described by the limitations above amount to a commercial interaction and managing interactions between people (i.e. ranking a plurality of repair facilities based on current service data, receiving a repair request to repair a vehicle, determining an adjusted ranking based upon current service data and a predicted current service capacity of the plurality of repair facilities, and determining and displaying one or more available repair facilities to service the repair request), the claims fall into the “certain methods of organizing human activity” grouping of abstract ideas.
As described in MPEP 2106.04(a)(2)(III), “[T]he "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” and “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” The limitations recited by the representative independent claims 1, 18 and 20 above, under the broadest reasonable interpretation and but for the use of generic computer components, cover concepts (e.g. observation, evaluation, judgment, and opinion) that can reasonably be performed in the human mind or by the human mind with the aid of simple tools such as pen and paper. For example, the “collect” and “receive” steps amounts to an observation, while the “generate workload rankings,” “predict service capacity of the plurality of repair facilities, with input of the plurality of current service data to adjust the workload rankings for the plurality of repair facilities…,” and “determine the one or more repair facilities…” steps would be considered evaluations, judgments, or opinions. Furthermore, displaying the one or more repair facilities (but for the use of generic computers/computer components, which is analyzed below) is analogous to outputting a list of the one or more repair facilities by a human, e.g. using pen and paper. Therefore, as the processes above described by the representative independent claims 1, 18 and 20 can be characterized as mental processes (i.e. observation, evaluation, judgment, and opinion), but for the recitation of generic computer components in the claims, the claims fall under the “mental processes” category of judicial exceptions (i.e. abstract ideas).
As claims 1, 18 and 20 are identified by the examiner as reciting concepts that fall under more than one abstract idea grouping (i.e. “certain methods of organizing human activity” and “mental processes”), the examiner considers the limitations together as a single abstract idea for the purposes of the Step 2A Prong Two and Step 2B analysis, in accordance with MPEP 2106.04(II)(B).
Step 2A Prong Two:
The judicial exception (i.e. abstract idea) recited in claims 1, 18 and 20 is not integrated into a practical application because the claims recite mere instructions to apply the abstract idea (i.e. ranking a plurality of repair facilities based on current service data, receiving a repair request to repair a vehicle, determining an adjusted ranking based upon current service data and a predicted current service capacity of the plurality of repair facilities, and determining and displaying one or more available repair facilities to service the repair request) using generic computers/computer components (i.e. “A computer system…comprising at least one processor in communication with at least one memory device, the computer system in communication with a user computer device associated with a user, the at least one processor is programmed to…,” “execute… a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the computer computing device of claim 1; “computer-implemented method…performed by one or more processors in communication with a memory,” “executing…a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the user computing device of claim 18; and “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by a computer system…including one or more processors and a memory, the computer-executable instructions cause the one or more processors to…,” “execute… a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the user computing device of claim 20). See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.,” and that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application.
While the claims recite the execution of a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities to adjust a workload ranking based on the query and a plurality of input data (claims 1, 18 and 20), there is no indication in the claims or the specification that the claimed invention improves machine-learning technology itself, but instead recite generic machine learning models to receive various input data and generate an output (adjust workload rankings). See Recentive Analytics, Inc. v. Fox Corp., holding that “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Furthermore, that the machine learning models are trained on historical service data to predict service capacity describes the use of machine learning technology in its ordinary capacity. Further see Recentive Analytics, Inc. v. Fox. Corp., showing “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.” Thus, “executing” and using the machine learning models to predict service capacity and adjust workload rankings represents the use of existing machine learning technology and generic computer implementation (generic machine learning models) as a tool to apply the abstract idea. In addition, the use of the user computer device to provide a query and display information on an interactive user interface (i.e. display one or more determined repair facilities) also merely adds generic computer implementation to output information, rather than anything that integrates the abstract idea into a practical application. The additional elements recited in the claims do not represent an improvement to machine learning technology and/or the recited machine learning models, improve the functioning of any computers, user devices, or user interfaces, or otherwise improve any other technology. Therefore, because the claims, considered as a whole, do not recite anything that integrates the abstract idea into a practical application, the claims are directed to an abstract idea.
Step 2B:
Claims 1, 18 and 20 do not include additional elements, whether considered alone or as an ordered combination, that are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea) because as mentioned above, the claims recite mere instructions to apply the abstract idea (i.e. ranking a plurality of repair facilities based on current service data, receiving a repair request to repair a vehicle, determining an adjusted ranking based upon current service data and a predicted current service capacity of the plurality of repair facilities, and determining and displaying one or more available repair facilities to service the repair request) using generic computers/computer components (i.e. “A computer system…comprising at least one processor in communication with at least one memory device, the computer system in communication with a user computer device associated with a user, the at least one processor is programmed to…,” “execute… a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the computer computing device of claim 1; “computer-implemented method…performed by one or more processors in communication with a memory,” “executing…a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the user computing device of claim 18; and “At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by a computer system…including one or more processors and a memory, the computer-executable instructions cause the one or more processors to…,” “execute… a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities,” “a user computer device,” and “an interactive user interface” displayed on the user computing device of claim 20).
As mentioned above, the recited execution of a plurality of machine-learning models trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities to adjust a workload ranking based on the query and a plurality of input data (claims 1, 18 and 20) does not improves machine-learning technology itself, but instead recites generic machine learning models to receive various input data and generate an output (adjust workload rankings). See Recentive Analytics, Inc. v. Fox Corp., holding that “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Furthermore, that the machine learning models are trained on historical service data to predict service capacity describes the use of machine learning technology in its ordinary capacity. Further see Recentive Analytics, Inc. v. Fox. Corp., showing “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.” Thus, “executing” and using the machine learning models to predict service capacity and adjust workload rankings represents the use of existing machine learning technology and generic computer implementation (generic machine learning models) as a tool to apply the abstract idea. In addition, the use of the user computer device to provide a query and display information on an interactive user interface (i.e. display one or more determined repair facilities) also merely adds generic computer implementation to output information, rather than anything that integrates the abstract idea into a practical application. The additional elements recited in the claims do not represent an improvement to machine learning technology and/or the recited machine learning models, improve the functioning of any computers, user devices, or user interfaces, or otherwise improve any other technology. The additional elements recited in the claims do not represent an improvement to machine learning technology and/or the recited machine learning models, improve the functioning of any computers, user devices, or user interfaces, or otherwise improve any other technology. Considering the additional elements as an ordered combination does not add significantly more than the abstract idea.
Dependent Claims 2-9, 11-17, 19, and 21:
Claims 2-9, 11-17, 19, and 21 are directed to the same abstract idea as independent claims 1 and 18 above without significantly more, as they do not recite anything that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea.
Claims 2, 14, and 19 recite the following limitations, which further describe the abstract idea and recite mere instructions to apply the abstract idea using generic computers/generic computer implementation (underlined): “wherein the at least one processor is further programmed to receive the plurality of current service data from a plurality of computer devices associated with the plurality of repair facilities” (claim 2); “the at least one processor is further programmed to generate a user interface to provide vehicle repair information based on a user selected geographic area” (claim 14 – which uses a generic user interface to display an output); and “receiving the plurality of current service data from a plurality of computer devices associated with the plurality of repair facilities” (claim 19).
Claims 3-6, 8, 11-12, 17, and 21 recite the following limitations, which do not add any additional elements beyond those already addressed above (e.g. the at least one processor), but merely further describe the abstract idea being carried out above by reciting limitations for: “wherein the plurality of current service data includes a current workload of repairing a plurality of vehicles…” (claim 3); “automatically remove a repair facility from a list…” (claim 4); “remove a repair facility from the one or more determined repair facilities…” (claim 5); “receive a plurality of performance data from the plurality of repair facilities…” (claim 6); “determine a condition of the user vehicle to be repaired; and re-rank the plurality of repair facilities…” (claim 8); “reduce a workload ranking for a repair facility…” (claim 11); “determine that a repair facility is over capacity…” (claim 12); and “rank the plurality of repair facilities in the corresponding geographic regions” (claim 17); and “wherein the plurality of current service data includes a reduced number of variables then the plurality of historical service data” (claim 21).
Claims 6, 7, 9, 11 and 13 recite limitations to: “retrain the plurality of machine-learning models based upon the plurality of performance data” (claim 6); “collect a plurality of updated current service data for the plurality of machine-learning models; update the plurality of machine-learning models with the plurality of updated current service data; and execute the plurality of machine-learning models with the plurality of updated current service data to generate an updated plurality of workload rankings” (claim 7); “wherein the plurality of current service data includes a plurality of vehicle repair information, and wherein the at least one processor is further programmed to: receive, as output from the plurality of machine-learning models, the respective predicted current service capacity for each of the plurality of repair facilities based upon the vehicle repair information” (claim 9); “when the one or more machine-learning models determines that the repair facility is over capacity” (claim 11); and “update the plurality of machine-learning models with the plurality of current service data” (claim 13). These limitations do not add anything that indicates an improvement to machine-learning models or machine-learning technology, but instead describe limitations that are consistent with the ordinary functioning of basic machine learning models (training, re-training, updating, and executing the machine learning models to receive an input and generate an output) and use generic machine learning implementation to apply the abstract idea.
Claims 15 and 16 describe the field of use of the machine-learning models (“wherein each model of the plurality of machine-learning models represents a different geographic region” of claim 15, and “wherein each machine-learning model is configured to model a respective plurality of repair facilities in the corresponding geographic region” of claim 16). Thus, the limitations of claims 15-16 at best generally link the performance of the abstract idea to a particular technical environment, but do not add anything that integrates the abstract idea into a practical application or adds significantly more.
Therefore, claims 1-9 and 11-21 are ineligible under § 101.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 3, 9, 14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth).
Claim 1: Kelsh teaches:
A computer system for real-time monitoring of a workload for a plurality of repair facilities (Kelsh: ¶ 0030, ¶ 0196-0198 and Fig. 10 showing computing system implementing methods described herein, including one or more processors in communication with memory and executing instructions stored in memory; ¶ 0039-0041, ¶ 0139-0142 showing “Find a Repair Shop” functionality and receiving information on repair shop availability),
the computer system comprising at least one processor in communication with at least one memory device (Kelsh: ¶ 0030, ¶ 0196-0198 memory storing instructions executed by processor),
the computer system in communication with a user computer device associated with a user (Kelsh: Fig. 1, ¶ 0022, ¶ 0033 showing user device 120 in communication with pre-FNOL system computer including at least repair clearinghouse server), the at least one processor is programmed to:
generate workload rankings for a plurality of repair facilities in communication with the computer system (Kelsh: ¶ 0141 showing “each of the repair shops may list availability based on the repair colors (e.g., green, yellow, and red)…” and ¶ 0142 and ¶ 0182 showing “generate a sortable list…The sortable list may display each of the repair shops within the geographical location associated with the mobile device 120 of the user…The user of mobile device 120 may be able to sort the repair shop data comprised within the sortable list based on factors such as highest repair ranking, nearest location, repair color availability, and the like”; also see ¶ 0039, ¶ 0139-0141, ¶ 0189 showing repair shops provide availability data, e.g. ¶ 0141 “Additionally, each of the repair shops may list availability based on the repair colors (e.g., green, yellow, and red). For example, a repair shop proximate to the user's geographical location may have ten slots per month available for green repairs, seven slots per month available for yellow repairs, and two slots per month available for red repairs…For example, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway and/or scheduled, the forecasted repair completion time may be one day. Conversely, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway but six green repairs scheduled ahead of the vehicle, the forecasted repair completion time may be six days” and see ¶ 0039 showing “repair clearinghouse database 174 may store data associated with repair shops, car rental agencies, and tow truck companies such as profiles, user ratings, availability schedules, and the like”)
collect, from the plurality of repair facilities, a plurality of current service data for the plurality of repair facilities (Kelsh: ¶ 0141 “each of the repair shops may list availability based on the repair colors (e.g., green, yellow, and red)…a repair shop… may have ten slots per month available for green repairs, seven slots per month available for yellow repairs, and two slots per month available for red repairs. The forecasted repair completion time for each of the respective repair colors may vary within the approximate ranges based on the number of vehicles associated with the specific color currently undergoing repair and/or scheduled to be repaired. For example, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway and/or scheduled, the forecasted repair completion time may be one day. Conversely, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway but six green repairs scheduled ahead of the vehicle, the forecasted repair completion time may be six days”, and ¶ 0039 “repair clearinghouse database 174 may store data associated with repair shops, car rental agencies, and tow truck companies such as profiles, user ratings, availability schedules, and the like”);
receive, from a user computing device, a query requesting service for repairing a user vehicle provided by one or more of the repair facilities (Kelsh: ¶ 0181-0182 showing user selects, using their mobile device, a “Find a Repair Shop” option, or ¶ 0187-0188 showing user selects “Yes” to request assistance in identifying a repair shop to repair the damages to their vehicle)
With respect to the limitations:
execute, based at least in part upon content of the query, a plurality of machine-learning models, trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities, with input of the plurality of current service data
to adjust the workload rankings for the plurality of repair facilities based upon a predicted current service capacity of the plurality of repair facilities output from the plurality of machine-learning models
Kelsh teaches determining a plurality of service providers including repair shops in response to a user query and using current service data from the repair shops indicating availability to generate a ranking of the repair shops/service providers as per above (Kelsh: ¶ 0039, ¶ 0139-0142, ¶ 0182, ¶ 0189), wherein the list may be sorted, i.e. re-ranked according to various criteria such as availability for a repair color/repair type availability (Kelsh: ¶ 0142, ¶ 0182), and further teaches considering historical service data including user ratings (Kelsh: ¶ 0039). Thus while Kelsh teaches generating a listing of repair shops and ranking/sorting the plurality of repair shops by availability and forecasted repair completion times which is based at least in part on current service data and workloads of each repair shop, Kelsh does not explicitly teach executing, based at least in part upon content of the query, a plurality of machine-learning models, trained on a plurality of historical service data for the plurality of repair facilities to predict service capacity of the plurality of repair facilities, with input of the plurality of current service data, or adjusting the workload rankings for the plurality of repair facilities based upon a predicted current service capacity of the plurality of repair facilities output from the plurality of machine-learning models.
However, Zavesky teaches a system for allocating resources to perform a task according to predicted capacity, and teaches based on receiving a task request (Zavesky: ¶ 0019 “resource management equipment 150 receiving task request 105”), executing a plurality of machine learning models (Zavesky: ¶ 0045-0046 showing capacity prediction component can include a plurality of machine learning models, including an ensemble model) trained on a plurality of historical production data for a plurality of resources (Zavesky: ¶ 0047 “initial and subsequent training of ANN 575 can be based on collected production data stored in historical data store 525”; also see ¶ 0039, ¶ 0048, ¶ 0052 past activity or past results), in order to predict current service capacity of a plurality of worker resources available to perform the requested task (Zavesky: ¶ 0028-0029, ¶ 0040, ¶ 0051-0054 showing predicting capacities of worker resources to perform tasks; see ¶ 0047 showing the capacity prediction may be carried out by capacity prediction component as per above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the processes for use of machine learning models for predicting capacities associated with resources to be allocated to received tasks of Zavesky in the repair shop identification system of Kelsh with a reasonable expectation of success of arriving at the claimed invention, with the motivation to “efficiently predict capacities for performing tasks by resources” (Zavesky: ¶ 0017). Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to do so, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Still, Kelsh/Zavesky do not explicitly teach the use of current predicted service capacities to adjust the workload rankings for the plurality of service providers (which may pertain to repair shops, as per Kelsh above) based upon the predicted current service capacities.
However, Barth teaches re-ranking service providers based on updated performance data, including current capacity and/or likelihood that each service provider will quickly accept a service request (Barth: ¶ 0048 showing “Service provider data can be updated (422). The service provider data can be updated based on the obtained performance data. In this way, the performance of the service provider can be used to refine the generation of eligible service providers and/or the ranking of service providers as described herein. This re-ranking can be automatically performed in real-time or near real-time, thereby allowing for service providers to be targeted for service requests based on the current capacity of the service provider and/or the likelihood that the service provider will quickly accept a service request. The capacity of the service provider can also be updated based on the completion of service requests. Timing data associated with the performance of the service request can also be used to predict the future capacity of the service provider based on the particular services required for a service request”; note that as per ¶ 0041-0042 showing the ranking/re-ranking can occur responsive to a service request from a vehicle user, and see ¶ 0053, ¶ 0041, ¶ 0043 showing historical performance, historical service times, availability, etc. pertaining to the service providers). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the re-ranking of service providers based on current service data and information on service provider service capacities/likelihood that each service provider will accept a service request Barth in the repair shop identification system of Kelsh/Zavesky with a reasonable expectation of success of arriving at the claimed invention, with the motivation that “Service providers can be ranked based on their respective weighted scores to determine the optimal service provider …to which to assign the received service request” (Barth: ¶ 0057).
Kelsh, as modified above (such that as per Barth above, service providers may be reranked by available capacity to service the request), further teaches:
determine the one or more repair facilities having availability based upon the adjusted workload rankings of the plurality of repair facilities to provide the service for repairing the user vehicle (Kelsh: ¶ 0182, ¶ 0188 showing repair clearinghouse server generates a sortable list displaying each of the repair shops within the geographical location associated with the mobile device 120 of the user, starting with the nearest repair shop to the geographical location associated with the mobile device 120 and terminating with the repair shop furthest from the geographical location associated with the mobile device 120. The user of mobile device 120 may be able to sort the repair shop data comprised within the sortable list based on factors such as highest repair ranking, nearest location, repair color availability, and the like; wherein as per above, the repair color availability was determined based on the workload of each repair shop); and
cause the user computer device to display an interactive user interface including a list of the one or more determined repair facilities (Kelsh: ¶ 0142, ¶ 0182, ¶ 0188 showing the display of the list of repair shops is rendered on the user’s mobile device; also see ¶ 0139-0142 showing interactive user interface elements to find a repair shop)
Claim 3: Kelsh/Zavesky/Barth teach claim 1. Kelsh, as modified above, further teaches:
wherein the plurality of current service data includes a current workload of repairing a plurality of vehicles at each of the plurality of repair facilities including a number of vehicles requiring repair at each repair facility and types of repairs or services being provided for each vehicle (Kelsh: ¶ 0141 showing “each of the repair shops may list availability based on the repair colors (e.g., green, yellow, and red). For example, a repair shop proximate to the user's geographical location may have ten slots per month available for green repairs, seven slots per month available for yellow repairs, and two slots per month available for red repairs. The forecasted repair completion time for each of the respective repair colors may vary within the approximate ranges based on the number of vehicles associated with the specific color currently undergoing repair and/or scheduled to be repaired. For example, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway and/or scheduled, the forecasted repair completion time may be one day. Conversely, if a vehicle determined as a green repair schedules a repair at a repair shop with no green repairs underway but six green repairs scheduled ahead of the vehicle, the forecasted repair completion time may be six days. Thus, responsive to scheduling a repair with a repair shop, the repair time field 510B of screen 510 may indicate a repair completion time specific to the repair shop”)
Claim 9: Kelsh/Zavesky/Barth teach claim 1. Kelsh, as modified above, further teaches:
wherein the plurality of current service data includes a plurality of vehicle repair information (Kelsh: ¶ 0141 showing current number of repairs underway for specific types/colors of repairs)
With respect to the remaining limitation:
receive, as output from the plurality of machine-learning models, the respective predicted current service capacity for each of the plurality of repair facilities based upon the vehicle repair information
Kelsh teaches that the output availability for each repair shop is based upon a number of currently available slots for each repair shop in comparison to the number of vehicles of each repair type currently being repaired (Kelsh: ¶ 0141-0142, ¶ 0181-0182, ¶ 0188 showing displayed list of repair shops which may be sorted by availability, and availability shown on repair shop screens), but does not explicitly teach that the output on the availability/service capacity is predicted by a machine learning model.
However, Zavesky teaches receiving a predicted service capacity for a resource from a plurality of machine learning models (Zavesky: ¶ 0028-0029, ¶ 0040, ¶ 0051-0054 showing predicting capacities of resources to perform tasks; see ¶ 0045-0047 showing the capacity prediction may be carried out by capacity prediction component including a plurality of machine learning models). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the use of machine learning models to predict capacities associated with resources to be allocated to tasks of Zavesky (which is analogous art pertaining to matching resources to tasks) in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, for the same reasons described in the rejection of claim 1 above.
Claim 14: Kelsh/Zavesky/Barth teach claim 1. Kelsh, as modified above, further teaches:
wherein the at least one processor is further programmed to generate the user interface to provide vehicle repair information based on a user selected geographic area (Kelsh: ¶ 0142 “The sortable list may display each of the repair shops within the geographical location associated with the mobile device 120 of the user”; also see ¶ 0181, ¶ 0188, ¶ 0190 showing geographical location may also be extracted from user input data)
Claim 18: See the rejection of claim 1 above teaching analogous limitations. Kelsh further teaches A computer-implemented method for monitoring a plurality of repair facilities, the computer-implemented method performed by one or more processors in communication with a memory (Kelsh: ¶ 0030, ¶ 0196-0198 and Fig. 10 showing computing device implementing methods described herein, including one or more processors in communication with memory and executing instructions stored in memory).
Claim 20: See the rejection of claim 1 above teaching analogous limitations. Kelsh further teaches At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon (Kelsh: ¶ 0201 “one or more computer-readable storage media having computer-readable program code, or instructions, embodied in or on the storage media”), wherein when executed by a computer system for detecting and acting upon operator reliance to vehicle alerts (Kelsh: ¶ 0004-0006 showing system receives indication of vehicle damage and need for repair), the computer system including one or more processors and a memory, the computer-executable instructions cause the one or more processors to (Kelsh: ¶ 0030, ¶ 0196-0198 and Fig. 10 showing computing device implementing methods described herein, including one or more processors in communication with memory and executing instructions stored in memory).
Claims 2, 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20220207931 A1 to Hinduja et al. (Hinduja).
Claim 2: Kelsh/Zavesky/Barth teach claim 1. With respect to the following limitations, Kelsh teaches receiving current service data from a plurality of repair shops (Kelsh: ¶ 0141 and ¶ 0039), and Barth teaches a plurality of service provider systems in communication with a roadside assistance system (Barth: Fig. 2, ¶ 0026-0030 service provider systems 207). However, to the extent that Kelsh/Zavesky/Barth do not explicitly teach receiving the current service data from computers associated with the repair facilities, Hinduja teaches:
wherein the at least one processor is further programmed to receive the plurality of current service data from a plurality of computer devices associated with the plurality of repair facilities (Hinduja: Fig. 1 and ¶ 0026-0027 showing application server in communication with plurality of service center devices, wherein Fig. 2B further shows that application server 110 receives current/new ground condition data and operator availability data from each service center server)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the application server receiving current service data from the repair facility computers as taught by Hinduja in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation to solve the problems that “For servicing, vehicles may be required to be brought to a service center (i.e., a workshop or a garage)…At any given point in time, multiple vehicles may be present at the service center awaiting service. Therefore, a vehicle brought to the service center for servicing may not immediately be serviced by the technicians. Typically, no assurances are offered to the vehicle owners on when the servicing of the vehicle will commence or be complete…This leads to undesirable experience for the vehicle owner, requiring the vehicle owner to repeatedly follow-up with the service center to get the vehicle serviced. Delays in the service completion or delivery time may have deep ramifications (e.g., loss in earnings) for the vehicle owner if the vehicle is required for business purposes (e.g., on-demand cab services, delivery of goods, or the like). As a result, many owners or transport aggregators avoid or delay servicing their vehicles, compromising the safety and convenience of passengers traveling in the vehicles” (Hinduja: ¶ 0004), and therefore, “there exists a need for a technical and reliable solution that overcomes the abovementioned problems and ensures effective prediction of service completion times for servicing vehicles at a service center” (Hinduja: ¶ 0005).
Claim 6: Kelsh/Zavesky/Barth teach claim 1. With respect to the following limitations, Kelsh/Zavesky/Barth do not explicitly teach the following, however, Hinduja teaches:
wherein the at least one processor is further programmed to: receive a plurality of performance data from the plurality of repair facilities; and retrain the plurality of machine-learning models based upon the plurality of performance data (Hinduja: ¶ 0176-0177 showing receiving actual service completion times, i.e. performance data, as feedback, for re-training prediction model based on the feedback of actual service completion times)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included re-training the prediction model using actual service completion times as feedback of Hinduja in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation to solve the problems that “For servicing, vehicles may be required to be brought to a service center (i.e., a workshop or a garage)…At any given point in time, multiple vehicles may be present at the service center awaiting service. Therefore, a vehicle brought to the service center for servicing may not immediately be serviced by the technicians. Typically, no assurances are offered to the vehicle owners on when the servicing of the vehicle will commence or be complete…This leads to undesirable experience for the vehicle owner, requiring the vehicle owner to repeatedly follow-up with the service center to get the vehicle serviced. Delays in the service completion or delivery time may have deep ramifications (e.g., loss in earnings) for the vehicle owner if the vehicle is required for business purposes (e.g., on-demand cab services, delivery of goods, or the like). As a result, many owners or transport aggregators avoid or delay servicing their vehicles, compromising the safety and convenience of passengers traveling in the vehicles” (Hinduja: ¶ 0004), and therefore, “there exists a need for a technical and reliable solution that overcomes the abovementioned problems and ensures effective prediction of service completion times for servicing vehicles at a service center” (Hinduja: ¶ 0005).
Claim 13: Kelsh/Zavesky/Barth teach claim 1. With respect to the following limitations, Zavesky teaches updating the machine learning models with new data (Zavesky: ¶ 0049). However, to the extent Kelsh/Zavesky/Barth do not explicitly teach updating the machine learning models with current service data, Hinduja teaches:
wherein the at least one processor is further programmed to update the plurality of machine-learning models with the plurality of current service data (Hinduja: ¶ 0166-0167, ¶ 0185-0186 showing new ground truth data collected from the service facilities, which is used to update the trained prediction model; note as above, ¶ 0041 teaches the prediction model may include an ensemble model, i.e. combination of plurality of machine learning models);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included updating the trained machine learning models using new data as taught by Hinduja in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation to solve the problems that “For servicing, vehicles may be required to be brought to a service center (i.e., a workshop or a garage)…At any given point in time, multiple vehicles may be present at the service center awaiting service. Therefore, a vehicle brought to the service center for servicing may not immediately be serviced by the technicians. Typically, no assurances are offered to the vehicle owners on when the servicing of the vehicle will commence or be complete…This leads to undesirable experience for the vehicle owner, requiring the vehicle owner to repeatedly follow-up with the service center to get the vehicle serviced. Delays in the service completion or delivery time may have deep ramifications (e.g., loss in earnings) for the vehicle owner if the vehicle is required for business purposes (e.g., on-demand cab services, delivery of goods, or the like). As a result, many owners or transport aggregators avoid or delay servicing their vehicles, compromising the safety and convenience of passengers traveling in the vehicles” (Hinduja: ¶ 0004), and therefore, “there exists a need for a technical and reliable solution that overcomes the abovementioned problems and ensures effective prediction of service completion times for servicing vehicles at a service center” (Hinduja: ¶ 0005).
Claim 19: See the rejection of claim 2 above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20220180293 A1 to Cahalin et al. (Cahalin).
Claim 4: Kelsh/Zavesky/Barth teach claim 1. With respect to the limitation:
wherein the at least one processor is further programmed to automatically remove a repair facility from the list of the one or more determined repair facilities based upon the updated workload ranking of the repair facility
Kelsh teaches generating a sortable list of repair shops in a geographic area and sorting the repair shops based on repair availability (based on workload in comparison to capacity), i.e. updated workload ranking (Kelsh: ¶ 0141-0142, ¶ 0181-0182), and Barth teaches updating service provider rankings including excluding or removing service providers (Barth: ¶ 0046, ¶ 0061-0062) but Kelsh/Zavesky/Barth do not explicitly teach removing the facility from the displayed list based upon the workload/availability.
However, Cahalin teaches “maintaining, in real time, the available presence list by removing service providers as they become unavailable” (Cahalin: ¶ 0009; also at least ¶ 0011, ¶ 0046, ¶ 0051 showing available presence list of service providers). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the removal of a service provider from a ranked list of service providers of Cahalin in the repair shop identification system of Kelsh/Zavesky/Barth (such that the sortable list of repair shops described by Kelsh is modified to remove repair shops that become unavailable) with a reasonable expectation of success of arriving at the claimed invention, with the motivation that that by removing unavailable service providers, the “remaining service providers are continuously moved up the live list thereby increasing the probability of the remaining service providers being viewed and subsequently selected by other users” (Cahalin: ¶ 0007). It would also have been obvious to one of ordinary skill in the art before the effective filing date of the invention to do so, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20240320766 A1 to Ernst et al. (Ernst).
Claim 5: Kelsh/Zavesky/Barth teach claim 1. With respect to the limitation:
wherein the at least one processor is further programmed to remove a repair facility from the one or more determined repair facilities based upon the respective predicted current service capacity of the repair facility being below a threshold; and prevent display of the repair facility in the list on the user interface while the predicted current service capacity is below the threshold
Kelsh teaches identifying a sorted list of repair shops based on repair availability (which is based on capacity) (Kelsh: ¶ 0141-0142, ¶ 0181-0182), and Zavesky teaches identifying that the capacity of the worker resource can be evaluated as being below a threshold level of capacity (Zavesky: ¶ 0040), but Kelsh/Zavesky/Barth do not explicitly teach removing the facility/service provider from the list based upon the determined available capacity being below a threshold.
However, Ernst teaches downranking or removing a repair service provider from a list based upon determining that the service provider is currently unavailable (Ernst: ¶ 0135 “filter the service providers to remove and/or down-weight service providers that are predicted to be unavailable for requests”), which may be based on when a decline rate is above a threshold, i.e. the availability/capacity of the provider is below a threshold amount (Ernst: ¶ 0156 “a service provider with a decline rate over a threshold, such as 50%, 75%, etc. for a particular type of request, can be updated to remove that type of request from the service providers capabilities (e.g., when identifying service providers and/or filtering out service providers at 702)”, also see ¶ 0052). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the removal of a service provider from a list of service providers based on current unavailability (capacity) of Ernst in the repair shop identification system of Kelsh/Zavesky/Barth (such that the sortable list of repair shops described by Kelsh is modified to remove repair shops that become unavailable or have an accept rate below a threshold) with a reasonable expectation of success of arriving at the claimed invention, with the motivation to improve service provider recommendations (Ernst: ¶ 0158-0159). It would also have been obvious to one of ordinary skill in the art before the effective filing date of the invention to do so, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11: Kelsh/Zavesky/Barth teach claim 9. With respect to the limitation:
wherein the at least one processor is further programmed to reduce a workload ranking for a repair facility when the one or more machine-learning models determines that the repair facility is over capacity
Kelsh teaches identifying a sorted list of repair shops based on repair availability (which is based on capacity) (Kelsh: ¶ 0141-0142, ¶ 0181-0182), and Barth teaches re-ranking service providers at least in part based on capacity (Barth: ¶ 0046-0048, ¶ 0052) but Kelsh/Zavesky/Barth do not explicitly teach reducing or removing the service provider ranking on the list based upon being at or over determined available capacity.
However, Ernst teaches downranking or removing a repair service provider from a list based upon determining that the service provider is currently unavailable, i.e. out of capacity, for requests (Ernst: ¶ 0135 “filter the service providers to remove and/or down-weight service providers that are predicted to be unavailable for requests”; also see ¶ 0158-0159 showing declining requests, indicating a service provider is getting more requests than they have the capacity to handle). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the removal of a service provider from a list of service providers based on current unavailability (capacity) of Ernst in the repair shop identification system of Kelsh/Zavesky/Barth (such that the sortable list of repair shops described by Kelsh is modified to remove repair shops that are unavailable/over capacity) with a reasonable expectation of success of arriving at the claimed invention, with the motivation to improve service provider recommendations (Ernst: ¶ 0158-0159). It would also have been obvious to one of ordinary skill in the art before the effective filing date of the invention to do so, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 11,238,469 B1 to Talvola et al. (Talvola).
Claim 7: Kelsh/Zavesky/Barth teach claim 1. With respect to the limitations:
wherein the at least one processor is further programmed to: collect a plurality of updated current service data for the plurality of machine-learning models;
update the plurality of machine-learning models with the plurality of updated current service data;
and execute the plurality of machine-learning models with the plurality of updated current service data to generate updated workload rankings for the plurality of repair facilities
As seen in the rejection of claim 1 above, Kelsh teaches generating a list of repair shops according to current availability/workload data (Kelsh: ¶ 0141-0142, ¶ 0181-0182), Zavesky teaches using machine learning models to predict available capacity (Zavesky: ¶ 0028-0029, ¶ 0040, ¶ 0051-0054; ¶ 0045-0047 showing machine learning models), and Barth teaches using current service data to update a ranking of service providers (Barth: ¶ 0048) – but Kelsh/Zavesky/Barth do not explicitly teach machine learning models being updated using updated data in order to generate the updated service provider rankings.
However, Talvola teaches collecting updated service data, using the updated service data by machine learning forecasting models, and generating updated service provider rankings (Talvola: Col. 1: 61-66, Col. 4: 13-19 showing continuously/periodically generating service provider rankings by machine learning/artificial intelligence system, triggered by events such as an update of available data for such forecasts; and Col. 13: 28-47 showing use of updated data for updating and use by machine learning forecasting models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included updating machine learning models with new data in order to repeat a ranking of service providers of Talvola in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation that “automatically re-running of the training module with more data makes the model predictions increasingly better, that is this machine learning is improving the prediction results. This provides an automatic artificial intelligence pipeline of ever-improving predictive models” (Talvola: Col. 13: 43-47).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20210125252 A1 to Biggs et al. (Biggs).
Claim 8: Kelsh/Zavesky/Barth teach claim 1. Kelsh, as modified above, further teaches:
wherein the at least one processor is further programmed to: determine a condition of a vehicle to be repaired (Kelsh: Fig. 6A and ¶ 0150-0151 showing damage level selection field presenting to the user, wherein the user may enter the damage level of the vehicle through their mobile device); and
With respect to the limitation:
re-rank the plurality of repair facilities based upon the condition of the vehicle to be repaired
While Kelsh teaches that “The user of mobile device 120 may be able to sort the repair shop data comprised within the sortable list based on factors such as …repair color availability,” which corresponds to the damage level or the type of repair needed (Kelsh: ¶ 0142), Kelsh/Zavesky/Barth do not explicitly teach also ranking the repair facilities based on the condition of the vehicle.
However, Biggs teaches prioritizing/filtering, i.e. ranking repair facilities based on information about the damaged vehicle shell/unibody features, and each repair facility’s ability to accommodate the plurality of special circumstances pertaining to the repair needs of the vehicle (Biggs: at least Fig. 2, ¶ 0040-0050 showing steps 204-206). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the filtering of repair facilities for specialized vehicle repair requirements of Biggs in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation to address the problems that “many collision repair facilities are not necessarily equipped to handle all collision-based repairs for all modern vehicles…” (Biggs: ¶ 0007), in order to “facilitate a proper repair of the damaged vehicle” (Biggs: ¶ 0058) and “help ensure that a damaged vehicle is not only repaired, but repaired in a way that preserves the intended and designed-in safety operability of the vehicle's unibody” (Biggs: ¶ 0059).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over S 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20190188743 A1 to Phillips et al. (Phillips).
Claim 12: Kelsh/Zavesky/Barth teach claim 9. With respect to the limitation:
wherein the at least one processor is further programmed to determine that a repair facility is over capacity when an average time to completion is greater than or equal to 45 days
Kelsh teaches a repair facility having a maximum repair time range for repairs categorized as “red” from 25 to 50 days depending on the number of other vehicles scheduled for repair, i.e. the repair facility would be over capacity when the average time to completion is above 50 days (Kelsh: ¶ 0140-0141; noting that under the broadest reasonable interpretation 50 days is “greater than or equal to 45 days”) – but Kelsh/Zavesky/Barth do not explicitly teach the actual determination of a service/business being over capacity.
However, Phillips teaches determining that a demand for a service exceeds the current capacity to the provide the service, and identifying alternative service providers that are available to provide the service in the region (Phillips: ¶ 0080). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination that a service provider is over capacity of Phillips in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation “to quickly and efficiently adjust service provider resources to meet a demand for a service” (Phillips: ¶ 0015). It would also have been obvious to one of ordinary skill in the art before the effective filing date of the invention to do so, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over S 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), and further in view of US 20200365034 A1 to Barth et al. (Barth), and further in view of US 20120317087 A1 to Lymberopoulos et al. (Lymberopoulos).
Claim 15: Kelsh/Zavesky/Barth teach claim 1. With respect to the following limitations, Kelsh/Zavesky/Barth do not explicitly teach, however, Lymberopoulos teaches:
wherein each model of the plurality of machine-learning models represents a different geographic region (Lymberopoulos: ¶ 0004-0005, ¶ 0049 and Fig. 3, and ¶ 0053 applying different ranking models to different respecting geographic regions; ¶ 0043 the generated ranking models use machine learning techniques, i.e. are machine-learning models)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the selection of machine learning models tailored for different geographic regions of Lymberopoulos in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, to achieve the benefit/motivation of “picking the ranking model that provides the best results for each region. For instance, for a specific set of regions (e.g., zip codes) in New York State, the state model might achieve the best performance. Conversely, for the Manhattan region of New York City, a New York City model might achieve better performance” (Lymberopoulos: ¶ 0005).
Claim 16: Kelsh/Zavesky/Barth/Lymberopoulos teach claim 15. With respect to the following limitation:
wherein each machine-learning model is configured to model a respective plurality of repair facilities in the corresponding geographic region
As per the rejection of claim 1, Kelsh teaches searching for and identifying a plurality of repair facilities within a geographic location/region in the user’s immediate area (Kelsh: ¶ 0139, ¶ 0142), while Zavesky teaches using a plurality of machine learning models for a resource capacity determination and allocation (see claim 1 above) – but Kelsh/Zavesky/Barth do not explicitly teach each machine learning model corresponding to respective service locations in a corresponding geographic region.
However, Lymberopoulos teaches identifying business/service provider locations by separate ranking models each corresponding to respective geographic regions (Lymberopoulos: ¶ 0004-0005, ¶ 0049 and Fig. 3, and ¶ 0053 applying different ranking models to different respecting geographic regions; ¶ 0043 the generated ranking models use machine learning techniques, i.e. are machine-learning models). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the selection of machine learning models tailored for different geographic regions of Lymberopoulos in the repair shop identification system of Kelsh/Zavesky/Barth/Lymberopoulos with a reasonable expectation of success of arriving at the claimed invention, for the same reasons discussed in the rejection of claim 15 above.
Claim 17: Kelsh/Zavesky/Barth/Lymberopoulos teach claim 16. With respect to the limitation:
wherein the at least one processor is further programmed to rank the plurality of repair facilities in the corresponding geographic regions
Kelsh teaches wherein the at least one processor is further programmed to rank the plurality of repair facilities in a geographical region surrounding the user’s geographical location (Kelsh: ¶ 0141-0142 showing sortable list of repair shops within the geographical region associated with the user), but Kelsh/Zavesky/Barth do not explicitly teach ranking a plurality of locations in a plurality of corresponding geographic regions.
However, Lymberopoulos teaches ranking a plurality of locations in a plurality of corresponding geographic regions (Lymberopoulos: Lymberopoulos: ¶ 0004-0005, ¶ 0049 and Fig. 3, and ¶ 0053 applying different ranking models to search results for different respecting geographic regions; ¶ 0034, ¶ 0036, ¶ 0038 showing the search results pertain to businesses or commercial locations). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the ranking of results for different geographic regions of Lymberopoulos in the repair shop identification system of Kelsh/Zavesky/Barth/Lymberopoulos with a reasonable expectation of success of arriving at the claimed invention, for the same reasons discussed in the rejection of claim 15 above.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over US 20180082379 A1 to Kelsh et al. (Kelsh) in view of US 20240037007 A1 to Zavesky et al. (Zavesky), further in view of US 20200365034 A1 to Barth et al. (Barth), and even further in view of US 20190087744 A1 to Schiemenz.
Claim 21: Kelsh/Zavesky/Barth teach claim 1. With respect to the following limitation, Kelsh teaches current and historical data associated with repair shops (Kelsh: ¶ 0039-0041, ¶ 0139-0142), and Zavesky teaches using data for training and analysis by a machine learning model (Zavesky: ¶ 0044-0046), but Kelsh/Zavesky/Barth do not explicitly teach the following. However, Schiemenz teaches:
wherein the plurality of current service data includes a reduced number of variables then the plurality of historical service data (Schiemenz: ¶ 0003-0005, ¶ 0029-0037, ¶ 0043 showing selecting variables for use within a machine-learning model, including removing one sub-set of variables from a machine learning model based on determining that certain variables are not relevant to the accuracy of the machine learning models outputs – which renders obvious the concept of removing variables from one subset of data in comparison to another subset of data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the reduction or removal of a number of variables in a subset of data of Schiemenz in the repair shop identification system of Kelsh/Zavesky/Barth with a reasonable expectation of success of arriving at the claimed invention, with the motivation to “reducing an amount of computational resources consumed by a machine-learning model” (Schiemenz: ¶ 0004), and “The variables are selected in a manner that minimizes the number of variables needed to provide a desired level of prediction accuracy. By minimizing the number of variables, the machine-learning model can operate on smaller amounts of data…enabling predictive analyses to be performed more efficiently” (Schiemenz: ¶ 0006).
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.
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/HUNTER MOLNAR/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628