DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 1/12/2026.
Claims 1, 13, and 18 have been amended.
Claims 1-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that the predictive model and weighting is a comparison of data for ranking roadside assistance providers, which can be done in the human mind given enough time and is additionally organizing human activity. The Applicant’s claims are merely using a general purpose computer to implement the abstract idea (See MPEP 2106). Applicant does not properly identify the additional elements. Applicant’s arguments are not persuasive.
The Examiner notes that the computing device/server triggering dispatch of a roadside assistance vehicle is generically telling a human user to go provide roadside assistance based on the request, where displaying data on a hardware device is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
The Examiner notes that the feedback is just additional human input data for further analyzing the service requests for ranking purposes, which further narrows the abstract idea. The Examiner points to MPEP 2106.05 which states “the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter”, where a narrow abstract idea is still an abstract idea. Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible under 2B. The Examiner respectfully disagrees. The Applicant does not properly identify the additional elements. The Examiner again asserts that running calculations on a general purpose computer and outputting the results does not improve the functioning of the computer, but rather merely uses the computer as a tool for implementing the abstract idea (See MPEP 2106.05). Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
The Examiner asserts that Balu et al. teaches the ranking of service providers word for word in at least Paragraph 0110. Applicant’s arguments are not persuasive.
The Examiner asserts that Balu et al. teaches the selecting of service providers word for word in at least Paragraph 0123. Applicant’s arguments are not persuasive.
The Examiner asserts that Balu et al. teaches the dispatch of service providers word for word in at least Paragraph 0034. Applicant’s arguments are not persuasive.
The Examiner asserts that Balu et al. teaches the monitoring of service providers word for word in at least Paragraph 0079. Applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-12 are directed toward a process, claims 18-20 are directed toward a product, and claims 13-17 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a roadside assistance provider selection system comprising: a processing device in communication with a network and receiving a roadside assistance request from a mobile device, the roadside assistance request comprising at least a geographic location; and a non-transitory database for a plurality of roadside assistance providers each associated with a corresponding service area; wherein the processing device executes one or more instructions that cause the processing device to perform the operations of: determining, based on the geographic location, a subset of roadside assistance providers associated with a service area corresponding to the roadside assistance request; calculating an estimated distance for each of the subset of roadside assistance providers based on the geographic location; receiving, based on the estimated distance for each of the subset of roadside assistance providers, one or more predicted values from a plurality of prediction models, wherein each of the one or more predicted values corresponds to an aspect of providing the requested roadside assistance and associated with each of the subset of roadside assistance providers; ranking the subset of roadside assistance providers based at least on one of the one or more predicted values from the plurality of prediction models; selecting, based on the ranked subset of roadside assistance providers, a roadside assistance provider to provide the requested roadside assistance in response to the roadside assistance request; transmitting a dispatch request comprising the roadside assistance request, wherein the processing device triggers dispatch of a roadside assistance vehicle associated with the selected roadside assistance provider by transmitting the dispatch request to a system of the selected roadside assistance provider monitoring a dispatch status of the roadside assistance vehicle and receiving feedback information comprising an actual value of the selected roadside assistance provider corresponding to the one or more predicted values; and adjusting, based on a comparison of the feedback information to the one or more predicted values, a weighted value assigned to each of the plurality of prediction models; and applying, the adjusted weighted values to weight the plurality of prediction models for generating ranked roadside assistance providers for subsequent roadside assistance requests in the service area (Organizing Human Activity and Mental Processes), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing predicted values to rank roadside assistance providers based on prediction models and received feedback by applying weighted values to the prediction models to dispatch service providers to service provider requests, which is managing how humans interact for the commercial purpose of providing roadside assistance. The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Mental Processes because the claimed limitations are analyzing predicted values to rank roadside assistance providers based on prediction models and received feedback by applying weighted values to the prediction models to dispatch service providers to service provider requests, which can be done in the human mind.
Dependent claims 2-12, 14-17, and 19-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a roadside assistance provider selection system comprising: a processing device in communication with a network; from a mobile device; and a non-transitory database for a plurality of roadside assistance providers; wherein the processing device executes one or more instructions that cause the processing device to perform the operations of: wherein the processing device triggers; to a system (claim 13)”; “by a computing device; at the computing device; by the computing device; wherein the computing device triggers; to a system (claim 1)”; “One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a server of a network, the computer process comprising; by the server; at the server; wherein the server triggers to a system (claim 18)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-12, 14-17, and 19-20 further narrow the abstract idea and dependent claims 2-3, 10-12, and 17 additionally recite “vehicle (claims 2-3, 11, and 17)”; “a user of a communication device (claim 10)”; “a computing device associated with the one or more third-party entities (claim 12)”; “a user of the mobile device (claim 17)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) because it is recited at such a high level of generality.
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1, 13, and 18 recite “a roadside assistance provider selection system comprising: a processing device in communication with a network; from a mobile device; and a non-transitory database for a plurality of roadside assistance providers; wherein the processing device executes one or more instructions that cause the processing device to perform the operations of: wherein the processing device triggers; to a system (claim 13)”; “by a computing device; at the computing device; by the computing device; wherein the computing device triggers; to a system (claim 1)”; “One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a server of a network, the computer process comprising; by the server; at the server; wherein the server triggers to a system (claim 18)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0071-0076 and Figures 1 and 8. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-12, 14-17, and 19-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2-3, 10-12, and 17 additionally recite “vehicle (claims 2-3, 11, and 17)”; “a user of a communication device (claim 10)”; “a computing device associated with the one or more third-party entities (claim 12)”; “a user of the mobile device (claim 17)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balu et al. (US 2020/0184591 A1) in view of Gupta et al. (US 2023/0186250 A1).
Regarding Claim 1: Balu et al. teach a roadside assistance provider selection system comprising (See Figure 1, Figures 5A-5D, and claim 1):
a processing device in communication with a network and receiving a roadside assistance request from a mobile device, the roadside assistance request comprising at least a geographic location (See Figure 1, Paragraph 0036 – “the location of the vehicle's breakdown”, Paragraph 0037 – “a request for a roadside assistance service”, and Paragraph 0043);
and a non-transitory database for a plurality of roadside assistance providers each associated with a corresponding service area (See Figure 1, Figure 2, Paragraph 0034 – “Information related to the registered service providers (and/or other service providers) may be stored in the database 225”, and Paragraph 0044 – “Service providers associated with a particular geographical service region may provide roadside assistance services”);
wherein the processing device executes one or more instructions that cause the processing device to perform the operations of (See Figure 1);
determining, based on the geographic location, a subset of roadside assistance providers associated with a service area corresponding to the roadside assistance request (See Paragraph 0044 – “Service providers associated with a particular geographical service region may provide roadside assistance services” and Paragraph 0103 – “determine whether the service provider as determined in step 507 is currently located within a threshold distance”);
calculating an estimated distance for each of the subset of roadside assistance providers based on the geographic location (See Paragraph 0044 – “Service providers associated with a particular geographical service region may provide roadside assistance services” and Paragraph 0103 – “determine whether the service provider as determined in step 507 is currently located within a threshold distance”);
receiving, based on the estimated distance for each of the subset of roadside assistance providers, one or more predicted values from a plurality of prediction models, wherein each of the one or more predicted values corresponds to an aspect of providing the requested roadside assistance and associated with each of the subset of roadside assistance providers (See Paragraph 0036, Paragraph 0044 – “Service providers associated with a particular geographical service region may provide roadside assistance services”, Paragraph 0068, Paragraph 0085, Paragraph 0103 – “determine whether the service provider as determined in step 507 is currently located within a threshold distance”, and Paragraph 0111 – “the selection engine 221 may estimate the determined service provider's cost or price for providing the requested roadside assistance service … the service provider may charge an hourly rate and/or a rate based on the distance traveled, in addition to a base amount”);
ranking the subset of roadside assistance providers based at least on one of the one or more predicted values from the plurality of prediction models (See Paragraph 0036, Paragraph 0041, Paragraph 0045 – “The roadside assistance unit 211 (e.g., the registration engine 223) may be configured to rank service providers based on various factors”, Paragraph 0058, Paragraph 0084, Paragraph 0110 – “the selection engine 221 may determine a weighted score for each service provider in the list, and may rank the service providers in the list based on their respective weighted scores, in order to determine the optimal service provider”, and Paragraph 0129 – “the service provider may agree to provide roadside assistance services at discount prices if the service provider is prioritized during the service provider ordering … the selection engine 221 may move the service provider to the top of the list or change the service provider to a higher ranking in the list”);
selecting, based on the ranked subset of roadside assistance providers, a roadside assistance provider to provide the requested roadside assistance in response to the roadside assistance request (See Paragraph 0045 – “the registration engine 223) may be configured to rank service providers based on various factors, so that optimal service providers are selected for rendering roadside assistance services”, Paragraph 0123 – “selecting the optimal service provider to assign the service request”, and Paragraph 0130 – “select a service provider (e.g., a top ranking service provider) from the list of ordered service providers”);
transmitting a dispatch request comprising the roadside assistance request, wherein the processing device triggers dispatch of a roadside assistance vehicle associated with the selected roadside assistance provider by transmitting the dispatch request to a system of the selected roadside assistance provider (See Figure 2, Paragraph 0034 – “may identify a registered service provider and dispatch the service provider to provide the requested roadside assistance service to the user”, and Paragraph 0037);
monitoring a dispatch status of the roadside assistance vehicle (See Figures 5A-5D and Paragraph 0079 – “the roadside assistance unit 211 may monitor the service provider's journey to and/or arrival at the location of the vehicle having the breakdown characteristic”);
receiving feedback information comprising an actual value of the selected roadside assistance provider corresponding to the one or more predicted values (See Figure 4, Paragraph 0068 – “training a machine learning algorithm or other supervised learning tool based on the dataset of previous events, and associated temporal, geographical, and vehicle system(s) data”, Paragraph 0083 – “receive user feedback pertaining to the service provider”, Paragraph 0085, Paragraph 0106 – “user's feedback on the previously provided roadside assistance service(s)”, Paragraph 0121, Paragraph 0123 – “selecting the optimal service provider to assign the service request”, and Paragraph 0130 – “select a service provider (e.g., a top ranking service provider) from the list of ordered service providers”);
“applying”, based on a comparison of the feedback information to the one or more predicted values, a weighted value assigned to each of the plurality of prediction models; and applying, the weighted values to weight the plurality of prediction models for generating ranked roadside assistance providers for subsequent roadside assistance requests in the service area (See Figures 5A-5D, Paragraph 0083 – “determine a weighted score for the service provider”, Paragraphs 0123-0124, Paragraph 0125 – “the selection engine 221 may assign a weight of four (4) to the on-time score, a weight of three (3) to the service provider's cost, and a weight of one (1) to the likelihood of acceptance, etc. In step 553, the roadside assistance unit 211 may determine a weighted score for the service provider”, and Paragraph 0127 – “the roadside assistance unit may send the determined weighted score, an assessment of the various factors of the weighted score, the service provider's user feedback, the service provider's job performance score, the service provider's on-time score, the service provider's estimated time of arrival, the service provider's estimated cost or extra costs, and/or the service provider's likelihood of accepting the request”).
Balu et al. do not specifically disclose “adjusting” a weighted value and applying, the “adjusted” weighted values. However, Gupta et al. further teach “adjusting” a weighted value and applying, the “adjusted” weighted values (See Figures 3C-3D, Paragraph 0033 – “the service recommendations are ranked and/or filtered”, Paragraph 0035, Paragraph 0037 – “user interface module 208 may filter out services that do not fit within the indicated value and omit them from the user interface”, Paragraph 0042, and claim 4).
The teachings of Balu et al. and Gupta et al. are related because both are analyzing roadside services to make recommendations to users. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the roadside service analysis system of Balu et al. to incorporate the filtering of Gupta et al. in order to ensure only relevant recommendations are made to users, thereby increasing customer satisfaction.
Regarding Claim 2: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein the roadside assistance request comprises a location identifier associated with a vehicle of the roadside assistance request, wherein the one or more predicted values is based on the location identifier associated with the vehicle (See Figure 1, Paragraph 0036 – “the location of the vehicle's breakdown”, Paragraph 0037 – “a request for a roadside assistance service”, and Paragraph 0043).
Regarding Claim 3: Balu et al. in view of Gupta et al. teach the limitations of claim 2. Balu et al. further teach: calculating an estimated distance for each of the plurality of roadside assistance providers based on the location identifier associated with the vehicle (See Paragraph 0044 – “Service providers associated with a particular geographical service region may provide roadside assistance services” and Paragraph 0103 – “determine whether the service provider as determined in step 507 is currently located within a threshold distance”).
Regarding Claim 4: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises an estimated time to arrival prediction model, the one or more predicted values comprising an estimated time to arrival associated with each of the roadside assistance providers generated by the estimated time to arrival prediction model (See Paragraph 0114 – “determine an estimated time of arrival (ETA) for the service provider as determined in step 533 to arrive at the disabled user vehicle from the service provider's current location … The estimated time of arrival may also be adjusted based on real-time traffic pattern. The selection engine 221 may determine the estimated time of arrival using any type of route planning system or algorithm”).
Regarding Claim 5: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises an acceptance prediction model, the one or more predicted values comprising an estimated probability of acceptance of an offer to provide the requested roadside assistance associated with each of the roadside assistance providers generated by the acceptance prediction model (See Paragraph 0037 – “a request for a roadside assistance service”, Paragraph 0039, Paragraphs 0111-0112 – “determine a likelihood of the service provider accepting the service request if the service request is assigned (e.g., forwarded) to the service provider … specific to the geographical region”, and Paragraph 0125 – “the selection engine 221 may assign a weight of four (4) to the on-time score, a weight of three (3) to the service provider's cost, and a weight of one (1) to the likelihood of acceptance, etc. In step 553, the roadside assistance unit 211 may determine a weighted score for the service provider”).
Regarding Claim 6: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises a cost prediction model, the one or more predicted values comprising an estimated cost associated with each of the roadside assistance providers to provide the roadside assistance generated by the cost prediction model (See Figures 5A-5D, Paragraph 0111 – “the selection engine 221 may estimate the determined service provider's cost or price for providing the requested roadside assistance service … the service provider may charge an hourly rate and/or a rate based on the distance traveled, in addition to a base amount”, and Paragraph 0125).
Regarding Claim 7: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises a multi-criteria prediction model, the one or more predicted values comprising an estimated time to arrival and an estimated probability of acceptance of an offer to provide the requested roadside assistance associated with each of the roadside assistance providers generated by the multi-criteria prediction model (See Paragraph 0037 – “a request for a roadside assistance service”, Paragraph 0039, Paragraphs 0111-0112 – “determine a likelihood of the service provider accepting the service request if the service request is assigned (e.g., forwarded) to the service provider … specific to the geographical region”, Paragraph 0114 – “determine an estimated time of arrival (ETA) for the service provider as determined in step 533 to arrive at the disabled user vehicle from the service provider's current location … The estimated time of arrival may also be adjusted based on real-time traffic pattern. The selection engine 221 may determine the estimated time of arrival using any type of route planning system or algorithm”, and Paragraph 0125).
Regarding Claim 8: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach wherein ranking the plurality of roadside assistance providers is further based on one or more ranking parameters each comprising a rule for ranking the plurality of roadside assistance providers based at least on the one of the one or more predicted values from the plurality of prediction models (See Paragraph 0045 – “The roadside assistance unit 211 (e.g., the registration engine 223) may be configured to rank service providers based on various factors”, Paragraph 0058, Paragraph 0110 – “the selection engine 221 may determine a weighted score for each service provider in the list, and may rank the service providers in the list based on their respective weighted scores, in order to determine the optimal service provider”, and Paragraph 0125).
Regarding Claim 9: Balu et al. in view of Gupta et al. teach the limitations of claim 1. Balu et al. further teach: determining an identifier received from the roadside assistance request; obtaining one or more weighted values associated with the identifier, each of the one or more weighted values corresponding to at least one of the one or more predicted values from the plurality of prediction models; and applying the one or more weighted values to the corresponding one or more predicted values prior to ranking the plurality of roadside assistance providers (See Figures 5A-5D, Paragraph 0083 – “determine a weighted score for the service provider”, Paragraphs 0123-0124, Paragraph 0125 – “the selection engine 221 may assign a weight of four (4) to the on-time score, a weight of three (3) to the service provider's cost, and a weight of one (1) to the likelihood of acceptance, etc. In step 553, the roadside assistance unit 211 may determine a weighted score for the service provider”, and Paragraph 0127 – “the roadside assistance unit may send the determined weighted score, an assessment of the various factors of the weighted score, the service provider's user feedback, the service provider's job performance score, the service provider's on-time score, the service provider's estimated time of arrival, the service provider's estimated cost or extra costs, and/or the service provider's likelihood of accepting the request”).
Regarding Claim 10: Balu et al. in view of Gupta et al. teach the limitations of claim 9. Balu et al. further teach wherein the identifier is associated with a user of a communication device from which the roadside assistance request is received (See Figure 1, Paragraph 0018 – “transmitting a request for assistance to a service center computing device”, Paragraph 0021, Paragraph 0130 – “The message may indicate that the user is requesting a roadside assistance service. The message may indicate information related to the service request, such as the user's identity, the location of the disabled user vehicle, the type of roadside assistance service requested, etc”).
Regarding Claim 11: Balu et al. in view of Gupta et al. teach the limitations of claim 9. Balu et al. further teach wherein the identifier is associated with a vehicle of the roadside assistance request (See Paragraph 0032 – “a vehicle identification number (VIN)”, Paragraphs 0036-0037, Paragraph 0051 – “the client user may input the desired search parameters into client computing device 208, and that the input may be detected and processed at the client portal server 209. These search parameters may include, but are not limited to, the vehicle identification 314A, vehicle type 314B, breakdown characteristics 314C, service rendered 314D, parts used 314E, service vehicle ID 314F”, and Paragraph 0055 – “a service provider ID may include, for example, a VIN, license plate, engine number, or other vehicle identifier or a vehicle belonging to a service provider”).
Regarding Claim 12: Balu et al. in view of Gupta et al. teach the limitations of claim 9. Balu et al. further teach wherein the identifier corresponds to one or more third- party entities and the one or more weighted values are received from a computing device associated with the one or more third-party entities (See Paragraph 0032 – “a vehicle identification number (VIN)”, Paragraphs 0036-0037, Paragraph 0051 – “the client user may input the desired search parameters into client computing device 208, and that the input may be detected and processed at the client portal server 209. These search parameters may include, but are not limited to, the vehicle identification 314A, vehicle type 314B, breakdown characteristics 314C, service rendered 314D, parts used 314E, service vehicle ID 314F”, and Paragraph 0055 – “a service provider ID may include, for example, a VIN, license plate, engine number, or other vehicle identifier or a vehicle belonging to a service provider”).
Regarding Claims 13-20: Claims 13-20 recite limitations already addressed by the rejections of claims 1-12 above; therefore the same rejections apply.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625