DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/30/2026 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 6/30/2026.
Claims 1, 10, 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. Applicant does not properly identify the additional elements. Predicting probabilities of acceptance using different parameters can be done in the human mind using pencil and paper, where the claimed general-purpose computer is merely being used as a tool for implementing the abstract idea (See MPEP 2106.05). The Examiner further asserts that performing the prediction of acceptance for providing roadside assistance is managing how humans interact (e.g. via roadside assistance), which is Organizing Human Activity. The Examiner asserts that “by the computing device” and adding the word “automatically” merely adds the words apply it with the judicial exception. The Examiner further notes that the word “automatically” is only recited once in Applicant’s originally filed specification in Paragraph 0025. Applicant’s arguments are not persuasive.
The Examiner further notes that this process is not fully automated as it requires a human to put in the initial request, a roadside service provider to accept the request, and then the roadside service provide to actually respond to the initial request. It is all performed by humans using general purpose computing devices. Applicant’s arguments are not persuasive.
The Examiner notes that the boost value calculations are part of the abstract idea as shown in the rejection below. The Examiner again asserts that running a calculation on a general purpose computer does not improve the computer itself, but rather merely uses the computer as a tool for implementing the abstract idea, which adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims provide significantly more. The Examiner respectfully disagrees. The Examiner points to Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward selecting and providing roadside assistance based on determined probabilities of acceptance and calculation of a boost value, where these techniques are merely being applied/calculated in a computing environment. Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. In addition, McRO had no evidence that the process previously used by animators is the same as the process required by the claims. The Applicant’s claimed limitations and originally filed specification provide no evidence that the claimed process/functions are any different than what would be done without a computer, where there are no adjustments to the mental process to accommodate implementation by computers. 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.
Applicant argues the cited prior art does not disclose the claimed boost value. The Examiner respectfully disagrees. Applicant’s arguments are not persuasive. The Examiner asserts that Lee teaches calculating, at a “boost” value prediction model, a “boost” value for at least one of the plurality of roadside assistance providers, wherein the “boost” value is based on one or more predicted values received from a plurality of prediction models (See Figure 3, Figure 6, columns 16-17 lines 43-67 and 1-5, column 34 lines 38-64, and column 35 lines 21-32), where columns 16-17 lines 43-67 and 1-5 specifically discloses adjusting values to increase probabilities of service providers accepting service requests with specific calculations done to determine these probabilities. The Examiner even notes that Lee teaches a score calculation system for dispatching vehicles/people to provide assistance, where a delivery is done on a road and can be considered roadside as well. The Examiner notes that if the claimed boost value is actually a specific calculation involving specifics on the roadside assistance beyond generic recitation (e.g. composite score based on one or more aspects), then the claims need to recite those details. Lee appears to specifically tech the claims limitation in view of BRI. Applicant’s arguments are not persuasive.
The Examiner recommends amending in more specific details of what the boost value and boost value prediction model actually entail to aid in overcoming the rejection.
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 1-9 are directed toward a process, claims 18-20 are directed toward a product, and claims 10-17 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a method for selecting a roadside assistance provider, the method comprising: receiving, from a database, data corresponding to each roadside assistance provider of a plurality of roadside assistance providers associated with a service area; determining, by a computing device and based on a roadside assistance request and for each of the plurality of roadside assistance providers associated with the service area, a predicted probability of acceptance of providing requested roadside assistance, wherein the service area is identified based upon a location identifier associated with at least one of a vehicle or a mobile device corresponding to the roadside assistance request, the roadside assistance request received from the mobile device over a network; calculating, by the computing device using a boost value prediction model, a boost value for at least one of the plurality of roadside assistance providers, wherein the boost value prediction model receives one or more predicted values from a plurality of prediction models as an input, wherein the boost value is based on the one or more predicted values and increases the predicted probability of acceptance of providing requested roadside assistance above a predetermined threshold value for at least one of the plurality of roadside assistance providers, 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 plurality of roadside assistance providers, and wherein the boost value is a composite score based on one or more aspects of providing the requested roadside assistance; selecting, by the computing device and based on the boost value and the one or more predicted values, a roadside assistance provider from the plurality of roadside assistance providers to provide the requested roadside assistance in response to the roadside assistance request; transmitting, by the computing device, a dispatch request comprising the location identifier and the roadside assistance request upon selection of the roadside assistance provider, wherein the computing device triggers dispatch of a roadside assistance vehicle associated with the selected roadside assistance provider by automatically transmitting the dispatch request to a selected roadside assistance provider system, wherein the dispatch request is transmitted in response to the computing device determining, with the boost value, the predicted probability of acceptance of providing requested roadside assistance is above the predetermined threshold value for the selected roadside assistance provider, and wherein the selected roadside assistance provider system initiates deployment of the roadside assistance vehicle upon receipt of the dispatch request; sending, by the computing device to the vehicle or mobile device corresponding to the roadside assistance request, roadside assistance provider data associated with the selected roadside assistance provider; and altering, based on feedback information associated with the selected roadside assistance provider, the boost value prediction model (Organizing Human Activity and Mental Processes), which are considered to be abstract ideas (See MPEP 2106.05). 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 probabilities of roadside assistance providers of accepting roadside assistance requests and altering the amount of money the provider would be paid until the probability of acceptance is above a threshold and transmitting the requests to the appropriate humans for interpretation, which is managing how humans determine how much to pay for a service and how the service should be handled, which is a commercial interaction. 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 probabilities of roadside assistance providers of accepting roadside assistance requests and altering the amount of money the provider would be paid until the probability of acceptance is above a threshold and transmitting the requests to the appropriate humans for interpretation, which can be done in the human mind.
Dependent claims 2-9, 11-17, 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 “from a database, by a computing device; the roadside assistance request received from the mobile device over a network; a vehicle or a mobile device; a selected roadside assistance provider system (claim 1)”, “roadside assistance provider selection system comprising: a processing device in communication with a network; from a mobile device; a non-transitory database; wherein the processing device executes one or more instructions that cause the processing device to perform operations; a vehicle or a mobile device; from the mobile device over the network; by the processing device; a selected roadside assistance provider system (claim 10)”, and “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 operations of; from a database; by the server; the vehicle or a mobile device; a selected roadside assistance provider system (claim 18)” would 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 2106.05) 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 2-9, 11-17, and 19-20 further narrow the abstract idea and recite no additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed structure merely adds the words to apply it with the judicial exception (See MPEP 2106.05).
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 2106.05). Further, method; System; and Product Independent claims 1, 10, and 18 recite “from a database, by a computing device; the roadside assistance request received from the mobile device over a network; a vehicle or a mobile device; a selected roadside assistance provider system (claim 1)”, “roadside assistance provider selection system comprising: a processing device in communication with a network; from a mobile device; a non-transitory database; wherein the processing device executes one or more instructions that cause the processing device to perform operations; a vehicle or a mobile device; from the mobile device over the network; by the processing device; a selected roadside assistance provider system (claim 10)”, and “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 operations of; from a database; by the server; the vehicle or a mobile device; a selected roadside assistance provider 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 0080-0084 and Figures 9. 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 2-9, 11-17, 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 and recite no additional elements, where the limitations merely amount 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 2106.05). 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1-20: Claims 1, 10, and 18 recite “the selected roadside assistance provider system”. There is insufficient antecedent basis for this claim limitation. Appropriate correction is required.
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 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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balu et al. (US 2020/0184591 A1) in view of Lee (US 11,507,988 B1).
Regarding Claim 1: Balu et al. teach a method for selecting a roadside assistance provider, the method comprising (See Figures 5A-5D and claim 1):
receiving, from a database, data corresponding to each roadside assistance provider of a plurality of roadside assistance providers associated with a service area (See Figure 1, Figure 2, Paragraph 0019, and Paragraph 0034 – “The registration engine 223 may be configured to register new service providers to the roadside assistance unit 211, and/or may be configured to manage registered service providers. When a user requests a roadside assistance service, the roadside assistance unit 211 may identify a registered service provider and dispatch the service provider to provide the requested roadside assistance service to the user. Information related to the registered service providers (and/or other service providers) may be stored in the database 225”);
determining, by a computing device and based on a roadside assistance request and for each of the plurality of roadside assistance providers associated with the service area, a predicted probability of acceptance of providing requested roadside assistance, wherein the service area is identified based upon a location identifier associated with at least one of a vehicle or a mobile device corresponding to the roadside assistance request, the roadside assistance request received from the mobile device over a network (See Figure 1, Figure 2, Paragraph 0037 – “a request for a roadside assistance service”, Paragraph 0039, and 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”);
calculating, by the computing device and using a value prediction model, a value for at least one of the plurality of roadside assistance providers, wherein the value prediction model receives one or more predicted values from a plurality of prediction models as an input, wherein the value is based on the one or more predicted values and increases the predicted probability of acceptance of providing requested roadside assistance (See Figures 5A-5D, Paragraph 0090 – “training a machine learning algorithm using the associated feature vectors”, Paragraphs 0111-0112 – “estimate the determined service provider's cost or price … estimate the distance that the service provider may travel to arrive at the disabled user vehicle … estimate the amount of time that the service provider may take to complete the requested service”, and 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”);
above a predetermined threshold value for at least one of the plurality of roadside assistance providers, and 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 plurality of roadside assistance providers (See Figures 5A-5D, Paragraph 0079, Paragraph 0103 – “within a threshold distance”, Paragraph 0105 – “below a threshold amount”, Paragraphs 0111-0112, Paragraph 0114, Paragraph 0131 – “within a time threshold”, and Paragraph 0133 – “a threshold degree”);
selecting, by the computing device and based on the value and the one or more predicted values, a roadside assistance provider from the plurality of roadside assistance providers to provide the requested roadside assistance in response to the roadside assistance request (See Paragraph 0110 – “determine a weighted score for each service provider in the list”, Paragraph 0118 – “the selection engine 221 may use the adjusted estimated time of arrival for calculating the weighted score for the service provider”, Paragraph 0125, and Paragraph 0130 – “the selection engine 221 may select a service provider (e.g., a top ranking service provider) from the list of ordered service providers”);
transmitting, by the computing device, a dispatch request comprising the location identifier and the roadside assistance request upon selection of the roadside assistance provider, wherein the computing device triggers dispatch of a roadside assistance vehicle associated with the selected roadside assistance provider by automatically transmitting the dispatch request to a selected roadside assistance provider system, wherein the dispatch request is transmitted in response to the computing device determining, with the value, the predicted probability of acceptance of providing requested roadside assistance is above the predetermined threshold value for the selected roadside assistance provider (See Figure 3D – “368”, Figure 3E – “370, 372, 374”, Figures 5A-5D, Paragraphs 0035-0037 – “dispatch the service provider to the location of the user to handle the malfunction and/or accident situation”, Paragraph 0069, Paragraph 0079, Paragraph 0103 – “within a threshold distance”, Paragraph 0105 – “below a threshold amount”, Paragraphs 0111-0112, Paragraph 0114, Paragraph 0131 – “within a time threshold”, and Paragraph 0133 – “a threshold degree”);
and wherein the selected roadside assistance provider system initiates deployment of the roadside assistance vehicle upon receipt of the dispatch request (See Figure 3D – “368”, Figure 3E – “370, 372, 374”, Figures 5A-5D, Paragraph 0037 – “dispatch the service provider to the location of the user to handle the malfunction and/or accident situation”, Paragraph 0037, and Paragraph 0079);
sending, by the computing device to the vehicle or mobile device corresponding to the roadside assistance request, roadside assistance provider data associated with the selected roadside assistance provider (See Figure 3D, Figure 3E, Paragraph 0079 – “One or both of the roadside assistance unit 211 and the client portal 208-209 may monitor the service provider arrival, e.g., via establishing a connection with the telematics system of the vehicle”, Paragraph 0127 – “sending one or more of the above-described assessments of the service provider to the client portal 208-209”, and Paragraph 0142);
altering, by the computing device and based on feedback information associated with the selected roadside assistance provider providing the requested roadside assistance, the value prediction model (See Paragraph 0110 – “determine a weighted score for each service provider in the list”, Paragraph 0118 – “the selection engine 221 may use the adjusted estimated time of arrival for calculating the weighted score for the service provider”, Paragraph 0125, Paragraph 0130 – “the selection engine 221 may select a service provider (e.g., a top ranking service provider) from the list of ordered service providers”, and Paragraph 0142 – “obtain feedback from the user regarding the roadside assistance service provided by the service provider”).
Balu et al. do not specifically disclose calculating, at a “boost” value prediction model, a “boost” value for at least one of the plurality of roadside assistance providers, wherein the “boost” value is based on one or more predicted values received from a plurality of prediction models, and wherein the “boost” value is a composite score based on one or more aspects of providing the requested roadside assistance. However, Lee further teaches calculating, at a “boost” value prediction model, a “boost” value for at least one of the plurality of roadside assistance providers, wherein the “boost” value is based on one or more predicted values received from a plurality of prediction models, and wherein the “boost” value is a composite score based on one or more aspects of providing the requested roadside assistance (See Figure 3, Figure 6, columns 16-17 lines 43-67 and 1-5 – “machine learning algorithms to dynamically adjust payout to deliverers in real-time … adjust payout to deliverers to increase the likelihood of deliverers accepting pending delivery orders”, column 34 lines 38-64 – “adjust the base fee using a pricing algorithm”, and column 35 lines 21-32 – “adjust the adjusted base fee based on the one or more features. By way of example, to minimize the probability of declination of delivery order offers to deliverers”).
The teachings of Balu et al. and Lee are related because both are analyzing requests from users and determining probabilities about service providers fulfilling the requests. 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 assistance analysis system of Balu et al. to incorporate the boost value of Lee in order to ensure the service request will be fulfilled by a service provider.
Regarding Claim 2: Balu et al. in view of Lee 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, and wherein the one or more predicted values comprising an estimated time to arrival associated with each of the plurality of roadside assistance providers generated by the estimated time to arrival prediction model (See Figures 5A-5D, Paragraphs 0111-0112 and 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”).
Regarding Claim 3: Balu et al. in view of Lee teach the limitations of claim 2. Balu et al. further teach wherein the estimated time to arrival is based on calculating an estimated distance for each of the plurality of roadside assistance providers based on the location identifier (See Figures 5A-5D, Paragraphs 0111-0112 and 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”).
Regarding Claim 4: Balu et al. in view of Lee teach the limitations of claim 2. Balu et al. further teach wherein the estimated time to arrival is based on determining a demand for roadside assistance associated with the service area corresponding to the roadside assistance request (See Paragraph 0036 – “analyzing the received data to predict future breakdowns of a vehicle, future roadside assistance service demands, and/or locations for potential breakdowns (e.g., forecast API 238)” and Paragraph 0041 – “predict the demand for roadside assistance services”).
Regarding Claim 5: Balu et al. in view of Lee teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises a cost prediction model, and wherein the one or more predicted values comprising an estimated cost associated with each of the plurality of roadside assistance providers to provide the roadside assistance generated by the cost prediction model (See Figures 5A-5D and Paragraph 0111 – “estimate the determined service provider's cost or price for providing the requested roadside assistance service”).
Regarding Claim 6: Balu et al. in view of Lee teach the limitations of claim 1. Balu et al. further teach wherein the plurality of prediction models comprises a multi-criteria prediction model, and wherein 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 plurality of roadside assistance providers generated by the multi-criteria prediction model (See Figures 5A-5D, Paragraph 0123 – “The weights for the factors may be determined based on a location associated with the roadside assistance unit 211”, 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”).
Regarding Claim 7: Balu et al. in view of Lee teach the limitations of claim 1. Balu et al. further teach a roadside assistance provider (See Figures 5A-5D and claim 1).
Balu et al do not specifically disclose adding the boost value to an offer value to generate a combined offer value; and transmitting the combined offer value to the selected assistance provider. However, Lee further teaches adding the boost value to an offer value for the selected assistance provider; and transmitting the combined offer value and the boost value to the selected assistance provider (See Figure 3, Figure 6, columns 16-17 lines 43-67 and 1-5 – “machine learning algorithms to dynamically adjust payout to deliverers in real-time … adjust payout to deliverers to increase the likelihood of deliverers accepting pending delivery orders”, column 34 lines 38-64 – “adjust the base fee using a pricing algorithm”, and column 35 lines 21-32 – “adjust the adjusted base fee based on the one or more features. By way of example, to minimize the probability of declination of delivery order offers to deliverers”).
The teachings of Balu et al. and Lee are related because both are analyzing requests from users and determining probabilities about service providers fulfilling the requests. 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 assistance analysis system of Balu et al. to incorporate the boost value of Lee in order to ensure the service request will be fulfilled by a service provider.
Regarding Claim 8: Balu et al. in view of Lee teach the limitations of claim 1. Balu et al. further teach wherein the value is calculated based on historical value calculations (See Paragraph 0112 – “service provider's historical acceptance rate”, Paragraph 0116 – “on-time score may be determined based on historical data associated with the service provider”, and Paragraph 0119 – “determine a job performance score for the service provider based on historical data”).
Balu et al. do no specifically disclose a boost value. However, Lee further teach a boost value (See Figure 3, Figure 6, columns 16-17 lines 43-67 and 1-5 – “machine learning algorithms to dynamically adjust payout to deliverers in real-time … adjust payout to deliverers to increase the likelihood of deliverers accepting pending delivery orders”, column 34 lines 38-64 – “adjust the base fee using a pricing algorithm”, and column 35 lines 21-32 – “adjust the adjusted base fee based on the one or more features. By way of example, to minimize the probability of declination of delivery order offers to deliverers”).
The teachings of Balu et al. and Lee are related because both are analyzing requests from users and determining probabilities about service providers fulfilling the requests. 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 assistance analysis system of Balu et al. to incorporate the boost value of Lee in order to ensure the service request will be fulfilled by a service provider.
Regarding Claim 9: Balu et al. in view of Lee teach the limitations of claim 1. Balu et al. further teach: ranking the plurality of roadside assistance providers based at least in part upon one of the one or more predicted values from the plurality of prediction models and based at least in part upon the value (See Figures 5A-5D, Paragraph 0045 – “rank service providers based on various factors”, and Paragraph 0110 – “rank the service providers in the list based on their respective weighted scores”).
Balu et al. do no specifically disclose a boost value. However, Lee further teach a boost value (See Figure 3, Figure 6, columns 16-17 lines 43-67 and 1-5 – “machine learning algorithms to dynamically adjust payout to deliverers in real-time … adjust payout to deliverers to increase the likelihood of deliverers accepting pending delivery orders”, column 34 lines 38-64 – “adjust the base fee using a pricing algorithm”, and column 35 lines 21-32 – “adjust the adjusted base fee based on the one or more features. By way of example, to minimize the probability of declination of delivery order offers to deliverers”).
The teachings of Balu et al. and Lee are related because both are analyzing requests from users and determining probabilities about service providers fulfilling the requests. 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 assistance analysis system of Balu et al. to incorporate the boost value of Lee in order to ensure the service request will be fulfilled by a service provider.
Regarding Claims 10-20: Claims 10-20 recite limitations already addressed by the rejections of claims 1-9 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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