DETAILED ACTION
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 .
Status of the Claims
Claims 1-20 were previously pending, claims 13-20 were withdrawn, and claims 1-12 were rejected in a non-final rejection February 27, 2026. In the Response, submitted on May 26, 2026, claims 1 and 11 were amened. Therefore, claims 1-20 are currently pending, and claims 1-12 are subject to the following final rejection.
Response to Arguments
Applicant’s remarks on Pages 8-10 of the Response, regarding the rejection of the claims under 35 U.S.C. 101, have been fully considered but are not found persuasive.
On Pages 8-9 of the Response, in discussing newly added additional elements, Applicant indicates that “‘live data…; preprocessing the live data to obtain a set of features associated with the request, wherein the set of features associated with the request consumable by both a first trained machine learning model and a second trained machine learning model; … inputting the set of features associated with the request into the first trained machine learning model…inputting the set of features associated with the request into the second trained machine learning model…’ the highlighted limitations of amended claim 1 do not recite an abstract idea…and therefore constitute “additional elements” that require further analysis under Step 2A, Prong Two.
Examiner agrees in-part. Examiner agrees that live data, and first and second trained machine learning models recite additional elements (as will be analyzed further below in the amended rejection). However, Examiner notes that “preprocessing the…data to obtain a set of features associated with the request, wherein the set of features associated with the request consumable by both a first … model and a second … model” reflect the abstract idea. That the data is live data, and that the first and second model are trained machine learning models reflects the additional elements (as will be discussed further below in the amended rejection).
On Page 9 of the Response, in discussing Step 2A, Prong Two, Applicant argues “claim 1 as a whole integrates any abstract idea into a practical application that improves data processing systems-namely, data processing systems for real-time request evaluation” and cites to portions of the specification.
Examiner disagrees and notes Para. [0022] of the Specification explains “the tenant may be unaware of the value of the service job before dispatching the service technician(s) to perform the service job. Several challenges may arise if the tenant is unaware of the value of the service job before dispatching the service technician(s) to perform the service job.” That is, the Specification discloses a business problem of tenants being unaware of the value of a service job prior to dispatch. Paras [0022]-[0023] of the Specification then merely disclose the subsequent effects of the business problem “the tenant may be unable to provide the customer with a quote before the service technician(s) arrives….the tenant may be unable to select the most appropriate service technician(s) to dispatch…” Para. [0025] then explains that the solution is through “improved techniques for predicting a service metric (e.g., a service job value) are desirable.” Therefore, while the Specification discloses a business process improvement, nothing in the specification explains a technical improvement in the technology of “data processing systems” as alleged. See MPEP 2106.05(a)(II) Improvements to Any Other Technology or Technical Field “…in Trading Technologies…the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.) (emphasis added). Thus, Applicant’s arguments are not found persuasive.
On Page 10 of the Response, Applicant then further re-iterates “Applicant’s claims reflect the techniques for improving data processing systems described in the Specification. For example, claim 1 requires ‘receiving, based on a tenant receiving a request, live data associated with the request; preprocessing the live data to obtain a set of features associated with the request, wherein the set of features associated with the request consumable by both a first trained machine learning model and a second trained machine learning model; generating, based on inputting the set of features associated with the request into the first trained machine learning model, a first value prediction associated with the request; determining at least one of a first conversion rate or a first average ticket value for at least one resource associated with a tenant to perform a service job associated with the request for a customer; generating, based on inputting the set of features associated with the request into the second trained machine learning model, a second value prediction associated with the request, wherein the second value prediction is associated with at least one different service job, and wherein the at least one different service job is predicted to be performed for the customer by the at least one resource subsequent to the at least one resource performing the service job for the customer; determining at least one of a second conversion rate or a second average ticket value for the at least one resource to perform the at least one different service job; and generating, based at least on the first value prediction, at least one of the first conversion rate or the first average ticket value, the second value prediction, and at least one of the second conversion rate or the second average ticket value, a comprehensive value prediction associated with the request.’ (Emphasis added).”
Examiner disagrees, as discussed above, nothing in the claims or specification disclose an improvement in live data or the first and second trained machine learning model, such that there is a technical improvement. As will be discussed further below in the detail, the additional element of live data amounts to no more than: merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)); and the use of a first and second trained machine learning model amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). Thus, Applicant’s arguments are not found persuasive.
Applicant’s remarks on Page 10 of the Response, regarding the rejection of claim 11 under 35 U.S.C. 112(b), have been fully considered and are persuasive in view of the amended claim.
Applicant’s remarks on Pages 11-14 of the Response, regarding the rejection of claims under 35 U.S.C. 103, have been fully considered but are moot in view of the amended claims and the amended references, or not found persuasive.
On Page 13 of the Response, Applicant argues “…the Office necessarily has not identified any portion of Qiu that discloses inputting such a set of features into the different two different trained machine learning models”.
Examiner notes the features upon which applicant relies (i.e., two different trained machine learning models) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, under broadest reasonable interpretation, a single trained machine learning model reads on a first trained machine learning model and a second machine learning model. If Applicant intends for the claim to recite two distinct or different trained machine learning models, then the claim should recite it as such. Thus, Qui teaches the amended limitations as discussed below in the amended rejection.
Claim Objections
Claims 7 and 8 are objected to for reciting “the first machine learning model” and “the second machine learning model”, and should recite “the first trained machine learning model” and “the second trained machine learning model” – as antecedent support from independent claim 1 recites “a first trained machine learning model” and “a second trained machine learning model.” Appropriate correction is required.
Claim 7 recites “the input data” in line 1. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the claim will be interpreted as reciting “input data.” Appropriate correction is required.
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.
Claim 7-8 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.
Claim 7 recites “the set of features” in line 2. It is unclear whether “the set of features” has antecedent support from: (i) “a set of features associated with the request” in line 4 of independent claim 1; or (ii) “a set of features associated with the service job” in line 2 of claim 7. For examination purposes, the claim will be interpreted as (ii).
Claim 8 is rejected by virtue of dependency.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-12 are directed to a method (i.e., a process), and therefore the claims all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claim 1 recites a method comprising: receiving, based on a tenant receiving a request, data associated with the request; preprocessing the data to obtain a set of features associated with the request consumable by both a first model and a second model; generating, based on inputting the set of features associated with the request into the first model, a first value prediction associated with the request; determining at least one of a first conversion rate or a first average ticket value for at least one resource associated with a tenant to perform a service job associated with the request for a customer; generating, based on inputting the set of features associated with the request into the second model, a second value prediction associated with the request, wherein the second value predication is associated with at least one different service job, and wherein the at least one different service job is predicted to be performed for the customer by the at least one resource subsequent to the at least one resource performing the service job for the customer; determining at least one of a second conversion rate or a second average ticket value for the at least one resource to perform the at least one different service job; and generating, based at least on the first value prediction, at least one of the first conversion rate or the first average ticket value, the second value prediction, and at least one of the second conversion rate or the second average ticket value, a comprehensive value prediction associated with the requests.
The limitations recited above recite the abstract idea of a certain method of organizing human activity (e.g., commercial interactions, following rules or instructions; and fundamental economic principles or practices). Therefore, the claim recites an abstract idea.
The mere recitation of live data, and using/inputting data into a first trained machine learning model and using/inputting data into a second trained machine learning model, does not take the claims out of the certain methods of organizing human activity grouping. Thus, the claim recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole: (i) merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)); (ii) generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)).
The claim recites the additional elements of: (i) live data; and (ii) a first trained machine learning model (for consuming data, receiving input data), and a second trained machine learning model (for consuming data, receiving input data).
The (i) live data is recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than merely including instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, or “apply it” (See MPEP 2106.05(f)).
The (ii) a first trained machine learning model (for consuming data, receiving input data), and a second trained machine learning model (for consuming data, receiving input) are recited at a high-level of generality such that, when viewed as whole/ordered combination, it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: generally linking the use of a judicial exception to a particular technological environment or field of use.
The same analysis applies here in 2B, i.e., merely including instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, or apply it; or generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent claims 2-10 merely recites details that narrow the previously recited abstract idea limitations. For these reasons, as described above with respect to claim 1, these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2-10 are also ineligible.
Step 2A, Prong One
Claim 11 recites wherein receiving the data associated with the request comprises receiving the data, from the tenant, in response to the tenant receiving the request -which further narrows the previously recited abstract idea.
Step 2A, Prong Two
Claim 11 recites the additional element of receiving the live data in real-time, which is recited at a high-level of generality such that, when viewed as a whole/ordered combination, amounts to no more than: reciting the words “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). Accordingly, the additional element, when viewed as a whole/ordered combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
The same analysis applies here in 2B, i.e., reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Step 2A, Prong One
Claim 12 recites causing display of the comprehensive value prediction associated with the service job- which further narrows the previously recited abstract idea.
Step 2A, Prong Two
Claim 12 recites the additional element of causing the display, via an interface of at least one computing device (associated with the tenant), which is recited at a high-level of generality such that, when viewed as a whole/ordered combination, amounts to no more than: reciting the words “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). Accordingly, the additional element, when viewed as a whole/ordered combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
The same analysis applies here in 2B, i.e., reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The 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 pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1, 3-4, 7-8, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0334616 to Qui et al. (hereinafter “Qiu”) in view of U.S. Patent Application Publication No. 2016/0292011 to Colson et al. (hereinafter “Colson”) and further in view of U.S. Patent Application Publication No. 2021/0019690 to Gordenker et al. (hereinafter “Gordenker”).
In regard to claim 1, Qui discloses receiving, based on a tenant receiving a request, live data associated with the request (Paras. 27, 36) (JVP system 100 may obtain real time information or live data from a job request (i.e., receiving live data associated with the request)…JVP can be generated or updated when the customer makes contact with the tenant to request service (i.e., based on a tenant revieing a request) associated with a job.)
Qui discloses preprocessing the live data to obtain a set of features associated with the request, wherein the set of features associated with the request consumable by both a first trained machine learning model and a second trained machine learning model (Paras. 27, 30, 33-34, 41-43, 45-46, and 50) (…preprocessing the live data can lead to generating a set of features associated with the job (i.e., preprocessing the live data to obtain a set of features associated with the request), the set of features consumable by the trained model (i.e., consumable by both a first trained machine learning model and a second trained machine learning model)…system substantially similar to system 100 may perform method 300 to prepare trained models for use in predicting JVPs and the like.)
Qui discloses generating, based on inputting the set of features associated with the request into the first trained machine learning model, a first value prediction associated with the request (Abst.; Paras. 36, 40, 42, 45-46, 49) (…a job value prediction (JVP) system 100 for generating a value prediction for a job (i.e., generating a first value prediction associated with the request) based on live data… The trained model may then be used to generate a predicted job value or JVP…. In some examples, training the model can include training a machine learning model to generate the trained model…the trained model can be applied to the set of features obtained from the live data to generate the job value prediction for the job (i.e., based on inputting the set of features associated with the request into the first trained machine learning model).)
Qui discloses determining at least one of …a first average ticket value for at least one resource associated with a tenant to perform a service job associated with the request for a customer (Paras.27, 36, 42 and 47) (FIG. 1 depicts a job value prediction (JVP) system 100 for generating a value prediction for a job… perform scheduling and assignment tasks for technician (i.e., a first… ticket value for at least one resource) …The JVP may be substantially similar to a respective direct or indirect job valuation value such as job revenue or profits…. a JVP may be generated instead by a substitute process, such as an aggregating and averaging process (not depicted) which generates a JVP (i.e., average ticket value)…The job request can be received by the tenant …from a customer about a job request (i.e., associated with a tenant to perform a service job associated with the request for a customer).)
Qui discloses generating, based on inputting the set of features associated with the request into the second trained machine learning model, a second value prediction associated with the request, wherein the second value predication is associated with at least one different service job (Abst.; Paras. 33, 36, 40, 42, 45-46, and 49) (…a job value prediction (JVP) system 100 for generating a value prediction for a job (i.e., a second value prediction associated with the request) based on live data… The trained model may then be used to generate a predicted job value or JVP…. In some examples, training the model can include training a machine learning model (i.e., using a second trained machine learning model) to generate the trained model… trained model can be applied to the set of features obtained from the live data to generate the job value prediction for the job (i.e., based on inputting the set of features associated with the request into the second trained machine learning model)…In some examples, the JVP can be provided to downstream services (e.g., for use in scheduling processes for future services provided by the same service provider or other service providers) (i.e., second value predication associated with the request, wherein the second value predication is associated with at least one different service job).)
Qui discloses determining at least one of …a second average ticket value for the at least one resource to perform the at least one different service job (Paras. 27, 33, 36 , 42 and 47) (FIG. 1 depicts a job value prediction (JVP) system 100 for generating a value prediction for a job… perform scheduling and assignment tasks for technician (i.e., for the at least one resource) …The JVP may be substantially similar to a respective direct or indirect job valuation value such as job revenue or profits (i.e., determining at least one of a second ticket value)…. a JVP may be generated instead by a substitute process, such as an aggregating and averaging process (not depicted) which generates a JVP (i.e., average ticket value)… The job request can be received by the tenant …from a customer about a job request… In some examples, the JVP can be provided to downstream services (e.g., for use in scheduling processes for future services provided by the same service provider or other service providers) (i.e., a second ticket value for the at least one resource to perform the at least one different service job.)
Qui does not explicitly disclose or teach, however, Colson teaches wherein the at least one different service job is predicted to be performed for the customer by the at least one resource subsequent to the at least one resource performing the service job for the customer (Para. 16) (Where a particular worker resource 106 (i.e., the at least one resource) previously performed multiple tasks for a particular client 104 (i.e., subsequent to the at least one resource performing the service job for the customer), the relationship information can indicate how often and/or to what extent the multiple tasks were successfully performed by the particular worker resource 106 for the particular client 104…. The task assignment server 122 may take into account such relationship information to assign further tasks, received from that particular client 104, to the same worker resource 106 that has had an excellent track record for successfully performing tasks for that particular client 104 (i.e., the at least one different service job is predicted to be performed for the customer by the at least one resource).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include taking into account the track record of a particular worker resource for a particular client as taught in Colson with the model for predicting a job value of Qui in order to provide a more accurate metric for assigning resources to a job.
As discussed above, Qui discloses the first value prediction and the first average ticket value, and the second value prediction and the second average ticket value. Qui in view of Colson does not explicitly disclose or teach, however, Gordenker teaches generating, based at least on the first value prediction, at least one of the first conversion rate or the first average ticket value, the second value prediction, and at least one of the second conversion rate or the second average ticket value, a comprehensive value prediction associated with the request (Paras. 21-23) (Edges of the bipartite graph may interconnect job nodes to timeslot nodes and each associated with a cost based on a calculated value of the job (i.e., first/second value prediction). In some examples, the edge costs may additionally be modified by a multiplier associated with the particular technician (associated with the particular timeslot) (i.e., first average ticket value for at least one resource/second average ticket value for the at least one resource)…. the solved bipartite graph provides a technician dispatching schedule…optimized for increased revenue realization (i.e., generating a comprehensive value predication associated with the request) because the lowest cost edges correlate to the highest value job assignments (e.g., job value multiplied by a technician multiplier).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the costs and edge costs of the bipartite graph of Gordenker with the model for predicting a job value of Qui in view of Colson in order to calculate a more accurate job value.
Examiner notes that as claim 1 only recites “determining at least one of a first conversion rate or a first average ticket value”, in lines 9-11 and “determining at least one of a second conversion rate or a second average ticket value” in lines 18-19, then under broadest reasonable interpretation the determining of a first conversion rate, and determining a second conversion rate is not required. Therefore, the limitations of dependent claim 5 (reciting “wherein determining the first conversion rate comprises”) and dependent claim 6 (reciting “wherein determining the second conversion rate comprises) are not required.
In regard to claim 3, Qui discloses wherein the first value prediction associated with the request comprises an expected value associated with the at least one resource performing the service job for the customer, and wherein the second value prediction associated with the at least one different service job comprises an expected value associated with the at least one resource performing the at least one different service job for the customer (Paras. 36 and 42) (FIG. 1 depicts a job value prediction (JVP) system 100 for generating a value prediction (i.e., expected value) for a job based on live data… The trained model may then be used to generate a predicted job value or JVP. The JVP can be used by the tenant to provide an estimate to the customer or requester who originated the job request (i.e., associated with the at least one resource performing the service job/different service job for the customer).)
In regard to claim 4, Qui discloses wherein the first machine learning model is trained to generate the first value prediction associated with the service job using at least one of: historical data associated with a type of the service job…wherein the second machine learning model is trained to generate the second value prediction associated with the at least one different service job using at least one of: historical data associated with a type of the at least one different service job… (Para. 49) (…training the model can include training a machine learning model to generate the trained model, where training the machine learning model comprises receiving a historical dataset a…comprising a historical record of features associated with the job).
In regard to claim 7, Qui discloses further comprising preprocessing the input data to obtain a set of features associated with the service job, the set of features consumable by the first machine learning model and the second machine learning model (Abst.) (A trained model associated with the tenant is retrieved, where the trained model is configured to generate a job value prediction for the job to be performed. The live data is preprocessed to obtain a set of features associated with the job, the set of features consumable by the trained model, and the trained model is applied to the set of features to generate the job value prediction for the job to be performed by the tenant.)
In regard to claim 8, Qui discloses wherein generating, using the first machine learning model, the first value prediction associated with the service job comprises inputting the set of features into the first machine learning model, and wherein generating, using the second machine learning model, the second value prediction associated with the at least one different service job, comprises inputting the set of features into the second machine learning model (Abst.) (A trained model associated with the tenant is retrieved, where the trained model is configured to generate a job value prediction for the job to be performed. The live data is preprocessed to obtain a set of features associated with the job, the set of features consumable by the trained model, and the trained model is applied to the set of features to generate the job value prediction for the job to be performed by the tenant.)
In regard to claim 10, Qui discloses sending, to the tenant, the comprehensive value prediction associated with the service job (Para. 33) (The JVP can be provided to the service provider or tenant.)
In regard to claim 11, Qui discloses wherein receiving the live data associated with the request comprises receiving the live data, in real-time and from the tenant, in response to the tenant receiving the request (Para. 36) ( JVP system 100 may obtain real time information or live data from a job request…the job request can be made to a tenant… The job request can be received by the tenant (i.e., from the tenant, response to the tenant receiving the request).)
In regard to claim 12, Qui discloses causing, via an interface of at least one computing device associated with the tenant, display of the comprehensive value prediction associated with the service job (Paras. 36 and 47) (At step 208, the JVP is output to a downstream service….In some examples, the JVP is provided downstream to task scheduling and assignment services (i.e., associated with the tenant). Further, in some examples, the JVP and/or the inputs may be stored in a repository for later retrieval, such as during a training phase…. The job request can be received by the tenant in the form of a phone call, electronic communication (e.g., instant or text message, electronic mail, via an application programming interface (API)) (i.e., via an interface of at least one computing device associated with the tenant).)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Qui in view of Colson and further in view of Gordenker, as applied to claim 1, and further in view of U.S. Patent Application Publication No. 2005/0209943 to Ballow et al. (hereinafter “Ballow”).
In regard to claim 2, as discussed above, Gordenker teaches the comprehensive value prediction associated with the request. Qui in view of Colson does not explicitly disclose or teach, however Gordenker teaches that the comprehensive value prediction indicates expected current value associated with the at least one resource performing the service job for the customer (Paras. 21-23) (Edges of the bipartite graph may interconnect job nodes to timeslot nodes and each associated with a cost based on a calculated value of the job [assigned to a technician] (i.e., indicates expected current value associated with the at least one resource performing the service job for the customer).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the costs and edge costs of the bipartite graph of Gordenker with the model for predicting a job value of Qui in view of Colson in order to calculate a more accurate job value.
Qui in view of Colson and further in view of Godenker does not explicitly disclose or teach, however Ballow teaches that the comprehensive value predication indicates future value associated with the at least one resource (Abst; Paras. 63-64) (…calculating a Total Economic Profit (TEP) value based in part on the financial data, the TEP value including a current value component and a future value component).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the total economic profit of Ballow with the optimized revenue of Qui in view of Colson and further in view of Gordenker in order to calculate a more accurate revenue.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Qui in view of Colson and further in view of Gordenker, as applied to claim 1, and further in view of “Why are Dealerships so Reluctant to do Warranty Repairs” by Magliozzi et al., dated June 12, 2021 (hereinafter “Magliozzi”).
In regard to claim 9, as discussed above in regard to claim 1, Qui discloses generating the first value prediction. Qui in view of Colson and further in view of Gordenker, does not explicitly disclose or teach, however, Magliozzi teaches before generating the first value prediction, determining that the service job is not associated with a value of zero, wherein determining that the service job is not associated with a value of zero comprises one or more of: determining that the service job is not a warranty service job (Pages 1-2) (Why are Dealerships so Reluctant to do Warranty Repairs…But until warranty repairs generate income equivalent to nonwarranty repairs, there will always be dealers out there who will shirk the warranty work -- assuming they've got enough work that they can pick and choose….)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include excluding warranty repair work from Magliozzi with the job value of Qui in view of Colson and further in view of Gordenker in order to provide more lucrative repair jobs for the tenant (See Pages 1-2 of Magliozzi).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 4-6 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5-6, and 8-9 of copending Application No. 18,882,222 (hereinafter “‘222 Application”) in view of Qui, and further in view of Colson and even further in view of Gordenker. This is a provisional nonstatutory double patenting rejection.
Claim 1 Current Application
Claim 1 '222 Application
A method comprising:
A method comprising:
receiving, based on a tenant receiving a request, live data associated with the request;
receiving resource information associated with a request;
preprocessing the live data to obtain a set of features associated with the request, wherein the set of features associated with the request consumable by both a first trained machine learning model and a second trained machine learning model;
generating from a set of features pre-processed from data associated with the request, using a first trained machine learning model, a first value prediction associated with the first service job for each resource in the list of resources; generating from the set of features pre-processed from data associated with the request, using a second trained machine learning model, a second value prediction associated with a second service job for each resource in the list of resources, wherein the second service job is predicted to be associated with the first service job and is performed subsequent to the first service job;
generating, based on inputting the set of features associated with the request into the first trained machine learning model, a first value prediction associated with the request;
generating from a set of features pre-processed from data associated with the request, using a first trained machine learning model, a first value prediction associated with the first service job for each resource in the list of resources;
determining at least one of a first conversion rate or a first average ticket value for at least one resource associated with a tenant to perform a service job associated with the request for a customer;
generating, based on inputting the set of features associated with the request into the second trained machine learning model, a second value prediction associated with the request, wherein the second value prediction is associated with at least one different service job, and wherein the at least one different service job is predicted to be performed for the customer by the at least one resource subsequent to the at least one resource performing the service job for the customer;
generating from the set of features pre-processed from data associated with the request, using a second trained machine learning model, a second value prediction associated with a second service job for each resource in the list of resources, wherein the second service job is predicted to be associated with the first service job and is performed subsequent to the first service job;
determining at least one of a second conversion rate or a second average ticket value for the at least one resource to perform the at least one different service job;
generating, based at least on the first value prediction, at least one of the first conversion rate or the first average ticket value, the second value prediction, and at least one of the second conversion rate or the second average ticket value, a comprehensive value prediction associated with the request.
generating, for each resource in the list of resources, a comprehensive value prediction based on the first value prediction, the second value prediction, and the weighting selection;
Claim 1 of the ‘222 Application discloses the limitations of claim 1 of the current application as discussed above, except for the above bolded claim language. However, Qui in view of Colson and even further in view of Gordenker teach the above bolded limitations of claim 1, as discussed above in the 35 U.S.C. 103 rejection, with the same obviousness rationale.
Claims 8 and 9 of the '222 Application disclose the limitations of claim 4.
Claim 5 of the '222 Application discloses the limitations of claim 5.
Claim 6 of the '222 Application discloses the limitations of claim 6.
Prior Art
The following prior art, made of record and not relied upon, is considered pertinent to Applicant’s disclosure:
U.S. Patent Application Publication No. 2020/00184405 to Mappus et al. (hereinafter “Mappus”). Mappus disclose a likelihood score for each pending installation job comprising a prediction of how likely it is that the installation job will require network-based technician work.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rupangini Singh whose telephone number is 571-270-0192. The examiner can normally be reached on Monday – Friday, 9:30 AM – 6:30 PM.
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/RUPANGINI SINGH/
Primary Examiner, Art Unit 3628
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