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 .
DETAILED CORRESPONDENCE
Status of Claims
Claims 1, 2 have been amended.
No claims have been cancelled.
No claims have been added.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 112(a) as follows:
(Claim 1)
(2) operating on a second machine learning model using an analysis of a comparison of the performance metrics of each service provider associated with prior jobs performed by each of the service providers, wherein the analysis includes:
(ii) determining a variable payment value based on the plurality of performance metrics;
(iii) continually updating the variable payment value for each type of job as new jobs of all the service providers are performed;
(iv) predicting which of the variable payment values for each of the prior type of jobs represents a type of job for which a service provider was approved resulting in increased claim expenses for the home services platform, and designating such variable payment value as a threshold for future approval of the same types of jobs;
(v) automatically approving the specific service provider for the repair job if the variable payment value associated with a previous same type of job as the approved scheduled job is above the threshold;
(vi) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired and retraining the first machine learning model to reflect the accuracy or inaccuracy of the prediction value determined for the service request;
(iv) determining in real-time based on the machine learning model of the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount.
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider
The Examiner asserts that there is no support for:
retraining a machine learning model;
the entirety of how the analysis is being used by the machine learning model, i.e. limitations (c)(2) and its sub-limitations (i) – (v); or
the determination of whether to select a particular service provider or selection of another service provider is based on an intended amount indicating a fraudulent amount (step iv).
Accordingly, the effective filing date for claims 1 and 2 is the filing date of the instant non-provisional application of November 27, 2024, which is a continuation-in-part of application 18/083970.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In regards to claim 1, the Examiner asserts that the following is new matter:
(iv) determining in real-time based on the machine learning model of the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount.
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider
Although limitations (v) and (vi), on their own, are supported, the Examiner asserts that when viewing the claimed invention, as a whole, the steps are new matter as they are being performed in response to step (iv). The Examiner looks to ¶ 124 of the applicant’s specification and asserts that there is no support for indicating a fraudulent amount nor that the indication is based on an intended amount that a service provider wants to charge or that the repair should not be made and, consequently, sending the service request to another service provider. ¶ 124 simply recites, “In embodiments, the host system 100 may compare the claims associated with one service provider versus others performing similar work, based on similar vectors (e.g., geography, systems and appliances, type of fix) to identify fraudulent claims or groups of claims.” This is insufficient support that a repair should not be made by a service provider because the intended amount they want to charge, an indication of fraudulent amount, that the fraudulent amount is based on the intended amount, indicating that the repair should not be made, and transmitting the request to another service provider.
Additionally, the Examiner asserts that the specification fails to satisfy the written description requirement because one of skill in the art would be unable to determine how an “intended amount to be charged” by itself would indicate a fraudulent amount. The specification only provides support that “claims”, which are not equivalent to charges, let alone intended charges, are compared and, presumably, based on some unknown threshold a fraudulent claim can be identified. However, this is based on a comparative analysis having to be performed, not simply looking at a single claim or “intended amount to be charged” and then making the conclusionary assessment that it is an indication of fraud based on only this single piece of information, i.e. claim of a service provider or “intended amount to be charged” of a service provider. Moreover, because the claimed invention only recites that once an “intended amount to be charged” is identified and immediately making the determination that it is an indication of a fraudulent amount, the Examiner does not understand how the goals and objectives of the invention are obtained because steps (iv) to (v) result in a never-ending circular loop, i.e. presumable, another service provider would be identified, the another service provider will provide an “intended amount to be charged”, a determination that it is a fraudulent amount is made, then a third service provider would be identified, and etc. There is no recitation of how, when, or why a service provider will ever be selected.
Claim Rejections - 35 USC § 112(b)
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 2 is 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 pre-AIA the applicant regards as the invention.
Claim 2 recites the limitation "the first algorithm" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim.
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 – 2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
(a) receiving a service request for a repair job to be performed on appliances or equipment at a home of a subscriber of the home services platform
receiving data inputs relating to:
(i) a make and model of respective appliances or equipment from respective service providers bound to prior repair jobs associated with the respective appliances or equipment;
(ii) a symptom associated with the respective appliances or equipment identified by the respective service providers during the repair job;
(iii) a course of action for repairing the respective appliances or equipment as performed by the respective service providers during the repair job;
(iv) a historical cost associated with the course of action for repairing each of the respective appliances or equipment including an out-of-pocket expense amount charged by the respective service providers to the subscriber; and
(v) a corresponding outcome of each of the prior repair jobs,
(c) predict whether each of the different types of repair jobs for which a respective prior service request was initially requested does require any of the service providers to be bound to that type of repair job in the future because the respective appliance or equipment was required to be repaired, and over time improving the accuracy of the predicting whether a future repair job is required to be bound to any of the service providers by:
(i) continually receiving updated sets of data inputs for each type of service request performed by each service provider;
(ii) determining a prediction value for each of the updated sets of data inputs, wherein higher prediction values are representative of an appliance or equipment needing to be serviced by a service provider and lower prediction values are representative of an appliance or equipment not needing to be serviced by a service provider;
(iii) determining a prediction value for the service request based on information associated with the service request provided by at least the subscriber regarding the appliance or equipment of the subscriber;
(iv) designating a threshold prediction value representative of when an appliance or equipment of the subscriber is required to be serviced;
(v) comparing the prediction value determined for the service request to the threshold prediction value and if the prediction value is equal to or greater than the threshold prediction value, allowing for the scheduling of the repair job for that appliance or equipment
(vi) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired [updating the analysis] to reflect the accuracy or inaccuracy of the prediction value determined for the service request; and
(d) designating a specific service provider for the repair job of the appliance or equipment by:
(i) transmitting a repair job request corresponding to the repair job via a user device of the specific service provider;
(ii) receiving confirmation via the user device that the specific service provider accepted the repair job request;
(iii) receiving data via the user device corresponding to a cost associated with the repair job;
(iv) determining in real-time based on the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount;
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider.
The invention is directed towards the abstract idea of appliance/equipment maintenance/servicing and scheduling, which corresponds to “Certain Methods of Organizing Human Activities” and “Mathematical Concepts” as it is directed towards steps that can be performed by humans with the aid of pen and paper, e.g., having a user/owner of an appliance/equipment determine their appliance/equipment requires servicing, searching, researching, and contacting a service provider, and scheduling a maintenance/servicing appointment with the service provider, as well as price prediction based on the available information about the appliance/equipment, user/owner, and service provider, which all are further based on the collection and comparison of information, to determine which amongst a plurality of potential service providers should perform the repair.
The limitations of:
(a) receiving a service request for a repair job to be performed on appliances or equipment at a home of a subscriber of the home services platform
receiving data inputs relating to:
(i) a make and model of respective appliances or equipment from respective service providers bound to prior repair jobs associated with the respective appliances or equipment;
(ii) a symptom associated with the respective appliances or equipment identified by the respective service providers during the repair job;
(iii) a course of action for repairing the respective appliances or equipment as performed by the respective service providers during the repair job;
(iv) a historical cost associated with the course of action for repairing each of the respective appliances or equipment including an out-of-pocket expense amount charged by the respective service providers to the subscriber; and
(v) a corresponding outcome of each of the prior repair jobs,
(c) predict whether each of the different types of repair jobs for which a respective prior service request was initially requested does require any of the service providers to be bound to that type of repair job in the future because the respective appliance or equipment was required to be repaired, and over time improving the accuracy of the predicting whether a future repair job is required to be bound to any of the service providers by:
(i) continually receiving updated sets of data inputs for each type of service request performed by each service provider;
(ii) determining a prediction value for each of the updated sets of data inputs, wherein higher prediction values are representative of an appliance or equipment needing to be serviced by a service provider and lower prediction values are representative of an appliance or equipment not needing to be serviced by a service provider;
(iii) determining a prediction value for the service request based on information associated with the service request provided by at least the subscriber regarding the appliance or equipment of the subscriber;
(iv) designating a threshold prediction value representative of when an appliance or equipment of the subscriber is required to be serviced;
(v) comparing the prediction value determined for the service request to the threshold prediction value and if the prediction value is equal to or greater than the threshold prediction value, allowing for the scheduling of the repair job for that appliance or equipment
(vi) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired [updating the analysis] to reflect the accuracy or inaccuracy of the prediction value determined for the service request; and
(d) designating a specific service provider for the repair job of the appliance or equipment by:
(i) transmitting a repair job request corresponding to the repair job via a user device of the specific service provider;
(ii) receiving confirmation via the user device that the specific service provider accepted the repair job request;
(iii) receiving data via the user device corresponding to a cost associated with the repair job;
(iv) determining in real-time based on the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount;
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and generic machine learning models. That is, other than reciting a generic processor executing computer code stored on a computer medium and generic machine learning models nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and generic machine learning models in the context of this claim encompasses a user can observe and collect information about how their appliance/equipment is operating, determine that they need to scheduling a servicing appointment with a service provider, search and research service providers, and schedule an appointment with a service provider. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and generic machine learning models, then it falls within the “Certain Methods of Organizing Human Activities” and “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium to communicate and store information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. searching, analyzing, and updating information related to the maintenance/service request, e.g., collecting and providing information associated with the repair request and service provider, determining costs, reviewing costs, determining whether to proceed with a repair, and notifying service providers. The generic processor executing computer code stored on a computer medium in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium and generic sensors can perform the insignificant extra solution steps of communicating and storing information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium are merely being applied to perform the steps that can be performed in the human mind or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium and generic.
Although the claim recites training and retraining a machine learning model the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a trained/retrained machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards appliance/equipment maintenance/servicing and scheduling. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and re-training are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). This is further evidenced by claim 2 and ¶ 505 of the applicant’s specification, which provide the additional support that the claimed invention is also directed towards “Mathematical Concepts” as the training and retraining process is performed using backpropagation algorithms, as well as generically reciting other techniques, i.e. the claimed invention is not improving upon machine learning or training/retraining techniques, but reciting generic technology at a high level of generality and applying it to the abstract idea. The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor executing computer code stored on a computer medium, sensors, and generic machine learning models to perform the steps of:
(a) receiving a service request for a repair job to be performed on appliances or equipment at a home of a subscriber of the home services platform
receiving data inputs relating to:
(i) a make and model of respective appliances or equipment from respective service providers bound to prior repair jobs associated with the respective appliances or equipment;
(ii) a symptom associated with the respective appliances or equipment identified by the respective service providers during the repair job;
(iii) a course of action for repairing the respective appliances or equipment as performed by the respective service providers during the repair job;
(iv) a historical cost associated with the course of action for repairing each of the respective appliances or equipment including an out-of-pocket expense amount charged by the respective service providers to the subscriber; and
(v) a corresponding outcome of each of the prior repair jobs,
(c) predict whether each of the different types of repair jobs for which a respective prior service request was initially requested does require any of the service providers to be bound to that type of repair job in the future because the respective appliance or equipment was required to be repaired, and over time improving the accuracy of the predicting whether a future repair job is required to be bound to any of the service providers by:
(i) continually receiving updated sets of data inputs for each type of service request performed by each service provider;
(ii) determining a prediction value for each of the updated sets of data inputs, wherein higher prediction values are representative of an appliance or equipment needing to be serviced by a service provider and lower prediction values are representative of an appliance or equipment not needing to be serviced by a service provider;
(iii) determining a prediction value for the service request based on information associated with the service request provided by at least the subscriber regarding the appliance or equipment of the subscriber;
(iv) designating a threshold prediction value representative of when an appliance or equipment of the subscriber is required to be serviced;
(v) comparing the prediction value determined for the service request to the threshold prediction value and if the prediction value is equal to or greater than the threshold prediction value, allowing for the scheduling of the repair job for that appliance or equipment
(vi) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired [updating the analysis] to reflect the accuracy or inaccuracy of the prediction value determined for the service request; and
(d) designating a specific service provider for the repair job of the appliance or equipment by:
(i) transmitting a repair job request corresponding to the repair job via a user device of the specific service provider;
(ii) receiving confirmation via the user device that the specific service provider accepted the repair job request;
(iii) receiving data via the user device corresponding to a cost associated with the repair job;
(iv) determining in real-time based on the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount;
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 2, as was discussed above, is directed towards “Mathematical Concepts” and reciting generic technology at a high level of generality and applying it to the abstract idea. The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for appliance/equipment maintenance/servicing and scheduling. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2 are rejected under 35 U.S.C. 103 as being unpatentable over Arensmeier et al. (US PGPub 2014/0074730 A1) in view of Blaney (6 Common Vendor Fraud Schemes Identify & Prevent _ Tipalti) in further view of Matsuoka et al. (US PGPub 20230076327 A1).
In regards to claim 1, Arensmeier discloses a system for providing home repair services in a home services platform, the system comprising one or more computers having non-transitory computer readable mediums having stored thereon instructions which, when executed by one or more processors of the one or more computers, causes the system to perform the steps of:
(a) receiving a service request for a repair job to be performed on appliances or equipment at a home of a subscriber of the home services platform
(¶ 16, 18, 19, 21, 27, 28, 59, 60, 61, 69, 94, 95, 182, 228, 230 wherein a monitoring service and system is provided to provide a customer with a maintenance and service monitoring plan to identify and/or predict problems and, when a problem is detected or predicted, the monitoring system will transmit an alert to a contractor to request servicing for the particular customer needing service, wherein the asset, i.e. appliance/equipment, in this case, HVAC system, is covered by a home protection plan, i.e. warranty or maintenance plan);
receiving data inputs relating to:
(i) a make and model of respective appliances or equipment from respective service providers bound to prior repair jobs associated with the respective appliances or equipment (¶ 22, 61, 162, 163, 234, 235 wherein the make and model of the appliance is received and inputted into the system);
(ii) a symptom associated with the respective appliances or equipment identified by the respective service providers during the repair job (¶ 27, 29, 61, 146, 185 wherein, during the repair job, the technician analyzes identified problems and predicted faults and inputted into the system);
(iii) a course of action for repairing the respective appliances or equipment as performed by the respective service providers during the repair job (¶ 28, 29, 184, 187 wherein, during the repair, a course of actions for repairing the appliance/equipment by the service provider during the repair job is inputted into the system);
(iv) a historical cost associated with the course of action for repairing each of the respective appliances or equipment including an out-of-pocket expense amount charged by the respective service providers to the subscriber (¶ 69, 70, 187 wherein historical cost for performing the repair, including out-of-pocket expenses (non-subsidized or non-warranty repairs) is inputted into the system); and
(v) a corresponding outcome of each of the prior repair jobs (¶ 29, 70, 187, 187 wherein repair history is inputted into the system),
(c) operating on a machine learning model of the received data inputs to predict whether each of the different types of repair jobs for which a respective prior service request was initially requested does require any of the service providers to be bound to that type of repair job in the future because the respective appliance or equipment was required to be repaired, and over time improving the accuracy of the predicting whether a future repair job is required to be bound to any of the service providers by:
(¶ 16, 29, 33, 34, 60, 63, 64, 69, 175, 187, 220, 225 wherein the system collects data from the monitored asset to determine if a failure or predict when a failure will occur; ¶ 16, 18, 19, 21, 27, 28, 29, 33, 34, 59, 60, 64, 69, 94, 95, 175, 182, 183, 187, 220, 225, 228, 230 wherein a monitoring service is provided for a customer that monitors various assets at the customer’s location. The assets are connected to sensors to monitor various aspects of the asset to determine if a fault has occurred and/or transmit operational/performance data to the central system for the purpose of analyzing the data and predict if a fault is likely. If a fault is detected or predicted, the system transmits a request for service to a contractor to service the faulty or soon to fault asset.
In other words, Arensmeier utilizes machine learning to collect and analyze historical data (which includes previous services, operational data, when components were installed, e.g., consumables), performance information, diagnostic data, urgency, and so forth to predict when servicing should be scheduled and if servicing needs to be performed a contractor will be scheduled and if servicing is not needed yet a contractor will not be scheduled.);
(i) continually receiving updated sets of data inputs for each type of service request performed by each service provider (¶ 68, 130, 146, 147 wherein the system receives real-time performance data of equipment during and after installation and during and after repair);
In regards to:
(ii) determining a prediction value for each of the updated sets of data inputs, wherein higher prediction values are representative of an appliance or equipment needing to be serviced by a service provider and lower prediction values are representative of an appliance or equipment not needing to be serviced by a service provider;
(iii) determining a prediction value for the service request based on information associated with the service request provided by at least the subscriber regarding the appliance or equipment of the subscriber;
(iv) designating a threshold prediction value representative of when an appliance or equipment of the subscriber is required to be serviced;
(v) comparing the prediction value determined for the service request to the threshold prediction value and if the prediction value is equal to or greater than the threshold prediction value, allowing for the scheduling of the repair job for that appliance or equipment
(¶ 29, 69, 70, 134, 162, 163, 172, 180, 182, 187, 189, 231 wherein service history information is used by the system to (as will be later discussed) predict failure and, if failure is determined (i.e. a high prediction value indicates potential failure and scheduling is needed while a low predication value indicates there is no potential failure and scheduling will not be needed), schedule servicing, wherein the service history information includes, at least, installation information; system modifications; historical service data; operational data of the appliance/equipment; baseline information; actions taken by a HVAC contractor (e.g., installing a consumable, which can further be used to determine when a consumable should be replaced in the future); observations made by a contractor that can be used for the next visit; repair history with corresponding data, servicing that was done, and corresponding prices;
¶ 16, 24, 26, 32, 33, 34, 146, 192, 195, 198 wherein sensor diagnostic data is received
¶ 27, 182, 187, 189 wherein appliance/equipment information is provided from a technician that was and/or currently assigned to service the appliance/equipment
¶ 16, 29, 33, 34, 60, 63, 64, 69, 175, 187, 220, 225 wherein the system collects data from the monitored asset to determine if a failure or predict when a failure will occur;
¶ 16, 18, 19, 21, 27, 28, 29, 33, 34, 59, 60, 64, 69, 94, 95, 175, 182, 183, 187, 220, 225, 228, 230 wherein a monitoring service is provided for a customer that monitors various assets at the customer’s location. The assets are connected to sensors to monitor various aspects of the asset to determine if a fault has occurred and/or transmit operational/performance data to the central system for the purpose of analyzing the data and predict if a fault is likely. If a fault is detected or predicted, the system transmits a request for service to a contractor to service the faulty or soon to fault asset.
In other words, Arensmeier utilizes machine learning to collect and analyze historical data (which includes previous services, operational data, when components were installed, e.g., consumables), performance information, diagnostic data, urgency, and so forth to predict when servicing should be scheduled and if servicing needs to be performed a contractor will be scheduled and if servicing is not needed yet a contractor will not be scheduled; and
(vi) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired and retraining, via an algorithm, the machine learning model to reflect the accuracy or inaccuracy of the prediction value determined for the service request (¶ 68, 130, 146, 147 wherein the system receives real-time performance data of equipment during and after installation and during and after repair;
¶ 175 wherein the machine learning model can be retrained to improve/refine its prediction by analyzing its prediction against updated observed data); and
(d) designating a specific service provider for the repair job of the appliance or equipment by:
(i) transmitting a repair job request corresponding to the repair job via a user device of the specific service provider (¶ 16, 19, 59, 60, 61, 94, 228, 230 wherein a repair job request is transmitted to a specific service provider’s user device);
(ii) receiving confirmation via the user device that the specific service provider accepted the repair job request (¶ 182, 183, 184 wherein a confirmation of accepting the request is received from a specific service provider by way of the provider scheduling an appointment);
(iii) receiving data via the user device corresponding to a cost associated with the repair job (¶ 187, 225, 232 wherein the system is in communication with the service provider’s device to determine the cost associated with the repair job);
(iv) determining in real-time based on the machine learning model of the received data inputs […] an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment […] (¶ 175, 187, 225, 232 wherein based on, at least, the analysis of the machine learning model the system determines a cost, in real-time, for the repair);
(v) […]; and
(vi) […]
Arensmeier discloses a system and method of leveraging machine learning to predict repair device failure for a customer and facilitate scheduling with a service provider to repair a failure or predicted failure. Although Arensmeier discloses that the system utilizes information about a service provider to assist customers with identifying and scheduling service with a service provider, Arensmeier fails to disclose all information that is associated with a service provider and that can be used with identifying a potential service provider amongst a pool of potential service providers and determining which service provider should not be recommended to perform a service request and which service provider should perform the service request.
To be more specific, Arensmeier fails to explicitly disclose:
(v) indicating in real-time via the user device to the specific service provider that the repair should not be made; and
(vi) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider
However, Matsuoka, which is also directed to monitoring the performance of equipment to predict failure and schedule servicing of the equipment, further teaches that one or more machine learning models can be trained and retrained to recommend service providers to a user at future points in time. Matsuoka teaches that the machine learning model(s) is trained on a plurality of information, such as, but not limited to, equipment performance, failure prediction, predicted future maintenance, user (member) preferences, and information associated with service providers. Matsuoka teaches that the machine learning model(s) utilizes this information to recommend to a user that servicing is recommended and should be scheduled with a service provider(s) that is also recommended by the machine learning model, wherein the service provider recommendation is based on service provider performance, availability, costs for performing service, reviews/ratings/or the like provided by users, prior experience that the user had with the service provider, whether the service provider would accept the job (which is based on whether they are qualified to perform the job or in the business associated with performing the job), bids provided by service providers, in which the analysis and its results are used by the system to facilitate scheduling of the job that is further based on the service provider accepting the job.
(For support see: ¶ 37, 38, 39, 40, 42, 43, 44, 46, 91, 93, 94, 180, 216, 217, 218, 219, 319, 368)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the machine learning servicing scheduling system and method of Arensmeier with the ability to evaluate a pool of service providers to determine which service provider should not be recommended and which service provider should be recommended, as taught by Matsuoka, as this results in an improved system and method can be provided for the matching of customers and service providers while also providing the customer with peace of mind that the service provider they have hired will provide a satisfactory job, as well as saving the customer time with regards to seeking out a service provider.
The combination of Arensmeier and Matsuoka discloses a system and method of leveraging machine learning to predict repair device failure for a customer and facilitate scheduling with a service provider to repair a failure or predicted failure. Although the combination of Arensmeier and Matsuoka discloses that the system utilizes information (e.g., cost information) about a service provider to assist customers with identifying and scheduling service with a service provider amongst a pool of potential service providers, the combination of Arensmeier and Matsuoka fails to disclose whether to compare a particular service provider’s cost against other service providers to determine if their cost estimation is in line with other service providers to determine whether the particular service provider is performing fraudulent activities.
To be more specific, the combination of Arensmeier and Matsuoka fails to explicitly disclose:
(iv) determining in real-time based on the machine learning model of the received data inputs that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicated of a fraudulent amount.
However, as best understood, in light of the rejections under 35 USC 112, Blaney, which is also discusses a centralized service provider management system, further teaches that it would have been obvious to identify vendors attempting to perform fraudulent activities based on, at least, how well a particular service provider’s cost compares with other service providers. Blaney teaches that service provider fraud is a known issue that exists in the industry and, accordingly, it would have been obvious and beneficial to identify fraud and, more specifically, referring to red flags that there is an indication of fraud and, consequently, not conducting business with that particular service provider. Blaney teaches that although it is difficult to eliminate fraud, there are many actionable ways to reduce the risk of service provider fraud, thereby protecting a user’s finances from nefarious service providers.
(For support see: Pages 1 – 11)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the machine learning servicing scheduling system and method of the combination of Arensmeier and Matsuoka with to identify fraudulent service providers based on, at least, how their costs compares to the costs of other service providers, as taught by Blaney, as this is one of many actionable ways to reduce the risk of service provider fraud, thereby protecting a user’s finances from nefarious service providers.
In regards to claim 2, the combination of Arensmeier, Matsuoka, and Blaney discloses the system of claim 1, wherein the first algorithm is a backpropagation algorithm and the retraining is performed using a computer (Matsuoka – ¶ 365, 367 wherein the one or more machine learning models can be trained and retrained using, for example, backpropagation and using a computer).
Response to Arguments
Applicant's arguments filed 6/27/2025 have been fully considered but they are not persuasive.
Double Patenting
The double patenting rejection has been withdrawn due to amendments.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The Examiner asserts that the claimed invention is not directed or akin to Example 47, Claim 3, of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence USPTO. The claimed invention is not improving technology, resolving an issue that arose in technology, or deeply rooted in technology. Reciting generic machine learning at a high level of generality to provide results for use in a business decision is not improving technology, but reciting generic technology at a high level of generality and applying it to the abstract idea. Example 47, Claim 3 is directed towards utilizing the results of machine learning to resolve an issue that is deeply rooted in technology and can only be performed with technology, in this case, blocking malicious data packets. Moreover, the claimed invention’s recitation of continually updating data inputs, retraining, and using a backpropagation algorithm coincides with Example 47, Claim 2 (See Page 6, ¶ 1, last ¶; Page 7, ¶ 1, ¶ 4, ¶ 5; Page 8, ¶ 1, 2; Page 9, last ¶ of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence)
Rejection under 35 USC 103
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Kondrardy et al. (US Patent 12,359,927 B2) – which is directed towards searching for potential service providers based on various criteria
Franke et al. (US PGPub 2017/0147991 A1) – which discloses the identification of fraudulent repair claims
Anonymous (The Most Common Procurement Fraud Schemes and their Primary Red Flags IACR