DETAILED ACTION
Claims 1-20 are pending in the present application and are under examination on the merits. This communication is the first action on the merits (FAOM).
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 4/24/2025. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 1/24/2025 are acceptable as filed.
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for generation of construction schedules and expense estimates. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 15-28) and "system" (claims 1-14).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
…collecting and pre-processing data…
…generating construction schedule and expense estimates associated with the construction schedules;
…generating a construction schedule and expense estimates associated with the constructions schedule using data generated by the machine learning software layer;
Independent claim 15 recites substantially similar claim language.
Dependent claims 2-14, and 16-28 recite the same or similar abstract idea(s) as independent claims 1 and 15 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-28 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to generation of construction schedules and expense estimates and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation by generation of construction schedules and expense estimates, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-28 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A machine learning system for automatic generation of construction schedules and expense estimates, comprising: a processor; a data integration software layer executed by the processor; the data integration software layer collecting and pre-processing data from an insurance claims estimation software application in communication with data integration software layer; a machine learning software layer executed by the processor, the machine learning software layer extracting a plurality of features from the data and training and deploying at least one predictive machine learning model for … a construction schedule generation software layer executed by the processor, the construction schedule software generation layer: / A machine learning method for automatic generation of construction schedules and expense estimates, comprising: " (claims 1 and 15) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "building system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-28 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of generation of construction schedules and expense estimates via a "machine learning system", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to generation of construction schedules and expense estimates.
Claims 1-28 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(A)(1) that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(A)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-8, 11-12, 15-17, 19-22, and 25-26 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by U.S. Patent Application Publication Number 2023/0368095 to Armstrong et al. (hereafter referred to as Armstrong).
As per claim 1, Armstrong teaches:
A machine learning system for automatic generation of construction schedules and expense estimates, comprising: a processor; a data integration software layer executed by the processor (Paragraph Number [0045] teaches Machine learning techniques for predicting a value of a repair job are disclosed. Paragraph Number [0022] teaches the techniques described herein relate to a system for predicting repair projects including one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by at least one processor, execute instructions, including: receiving a plurality of information for a geographic region... and generating a schedule for the workers to carry out the repair job).
the data integration software layer collecting and pre-processing data from an insurance claims estimation software application in communication with data integration software layer (Paragraph Number [0053] teaches data inputs 204 may optionally and/or additionally comprise insurance information 210. In some embodiments, generation system 202 may utilize data or information from one or more insurance carriers, or similar companies or businesses that process claims for damages and analyze the information to generate a recommendation or determination. Through the collecting and analyzing of insurance information 210, generation system 202 may base the recommendation or determination at least in part on the collected insurance information 210. By way of non-limiting example, in some embodiments, generation system 202 may be in communication with one or more insurance carriers or providers, with access to a database or other listing of incoming insurance claims, pending insurance claims, and/or processed insurance claims. Accordingly, in some embodiments, generation system 202 may analyze insurance information 210 to develop trends or statistics associated with one or more insurance carriers).
a machine learning software layer executed by the processor, the machine learning software layer extracting a plurality of features from the data and training and deploying at least one predictive machine learning model for generating construction schedule and expense estimates associated with the construction schedules (Paragraph Number [0046] teaches system 200 comprises a generation system 202 configured for receiving or obtaining raw data or information, analyzing the obtained information, and generating an estimate or recommendation. Generation system 202 may further comprise a machine learning algorithm which may be trained to monitor user actions or data inputs and determine trends or aid in generating a determination, recommendation, or estimate, and/or for automating certain processes. Paragraph Number [0056] teaches using the information from the insurance carrier, generation system 202 may determine the type of materials required for repairing the damage, how much material is needed, how many workers would be required for the project, and/or other determinations. Following the analysis, generation system 202 may generate a recommendation or determination, providing the repair business with project bidding information allowing the repair business to bid on the repair project in a timely manner. Optionally or additionally, the generated recommendation or determination may also allow the repair business to begin ordering materials or scheduling workers to respond to the repair project without delay. Paragraph Number [0104] teaches a machine learning model may output attribute weights that are then fine-tuned by the subject matter expert. Attributes may relate to financial information associated with the home and/or homeowner, demographic information of the homeowner, home features and/or value, or any other metric that may be useful in determining a likelihood of a roof repair request being obtained for the home).
a construction schedule generation software layer executed by the processor, the construction schedule software generation layer generating a construction schedule and expense estimates associated with the constructions schedule using data generated by the machine learning software layer (Paragraph Number [0063] teaches following the generated determination or recommendation, system 200 may automatically begin purchasing materials or scheduling workers to a location in preparation of a project. Paragraph Number [0081] teaches method 300 may be initiated by a potential project or lead submitted by a client; a project from an insurance carrier; or from a business operation inquiry prompted by a business for which generation system 202 may review and analyze to generate a recommendation, determination, or estimate that is specific to the project or lead. For example, a roofing repair project may be submitted by a homeowner to a roofing repair business, prompting method 300 to begin. In further embodiments, method 300 may be initiated automatically by generation system 202, including for example a systematic review of a business inventory in preparation of a spike of projects. Accordingly, an initial step 302 may comprise, the beginning of a project, which may comprise defining the scope of the project).
As per claim 15, Armstrong teaches:
A machine learning method for automatic generation of construction schedules and expense estimates, comprising: (Paragraph Number [0045] teaches Machine learning techniques for predicting a value of a repair job are disclosed. Paragraph Number [0022] teaches the techniques described herein relate to a system for predicting repair projects including one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by at least one processor, execute instructions, including: receiving a plurality of information for a geographic region... and generating a schedule for the workers to carry out the repair job).
The remainder of the claim language is substantially similar to that found in regard to claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claims 2 and 16, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the data integration software layer receives a completed insurance claim adjustment assignment from an insurance carrier computer system in communication with the processor (Paragraph Number [0053] teaches data inputs 204 may optionally and/or additionally comprise insurance information 210. In some embodiments, generation system 202 may utilize data or information from one or more insurance carriers, or similar companies or businesses that process claims for damages and analyze the information to generate a recommendation or determination. Through the collecting and analyzing of insurance information 210, generation system 202 may base the recommendation or determination at least in part on the collected insurance information 210. By way of non-limiting example, in some embodiments, generation system 202 may be in communication with one or more insurance carriers or providers, with access to a database or other listing of incoming insurance claims, pending insurance claims, and/or processed insurance claims. Accordingly, in some embodiments, generation system 202 may analyze insurance information 210 to develop trends or statistics associated with one or more insurance carriers. Paragraph Number [0104] teaches a machine learning model may output attribute weights that are then fine-tuned by the subject matter expert. Attributes may relate to financial information associated with the home and/or homeowner, demographic information of the homeowner, home features and/or value, or any other metric that may be useful in determining a likelihood of a roof repair request being obtained for the home (Examiner asserts that the insurance adjustment estimate is equivalent to a processed insurance claim)).
As per claims 3 and 17, Armstrong teaches each of the limitations of claims 1 and 2 and 15 and 16 respectively.
In addition, Armstrong teaches:
wherein the data integration software layer receives historical construction project and estimate data. (Paragraph Number [0117] teaches an estimator pane may display contact information for the employee who created, finalized, or approved the repair project estimation. An insurance company pane may display information associated with the insurance company. An adjuster pane may display contact information for the insurance adjuster at the insurance company. A timeline pane may display a timeline of events for the repair project. Paragraph Number [0132] teaches the random forest model is built with 100 estimators, although generally any number of estimators may be used. Accordingly, an end user may be able to view a prediction of the value of a job as precited by the random forest model which is trained based on historical data including the job type and the month in which the job was approved. Paragraph Number [0038] teaches reconditioning estimates may be generated based on historical data and the vehicle focused inspection).
As per claims 5 and 19, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the data integration software layer normalizes the data (Paragraph Number [0079] teaches web server 256 may also be communicatively coupled to an accounting module 272 for storing and/or retrieving financial data. Financial data from accounting module 272 may be transmitted to a data synchronization module 274 and therefrom to a data pipeline 276. Data synchronization module 274 may be an integration platform such as Pipedream that is configured to perform data synchronization, transformation, flattening, or any combination thereof of the financial data. From data synchronization module 274, the adjusted data may be passed to data pipeline 276, which may pull all data from system architecture 250 and transmit the data to a data warehouse 278. As shown, data pipeline 276 may receive data from first database 268).
As per claims 6 and 20, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the machine learning software layer extracts the plurality of features from an insurance adjustment estimate (Paragraph Number [0053] teaches data inputs 204 may optionally and/or additionally comprise insurance information 210. In some embodiments, generation system 202 may utilize data or information from one or more insurance carriers, or similar companies or businesses that process claims for damages and analyze the information to generate a recommendation or determination. Through the collecting and analyzing of insurance information 210, generation system 202 may base the recommendation or determination at least in part on the collected insurance information 210. By way of non-limiting example, in some embodiments, generation system 202 may be in communication with one or more insurance carriers or providers, with access to a database or other listing of incoming insurance claims, pending insurance claims, and/or processed insurance claims. Accordingly, in some embodiments, generation system 202 may analyze insurance information 210 to develop trends or statistics associated with one or more insurance carriers. Paragraph Number [0104] teaches a machine learning model may output attribute weights that are then fine-tuned by the subject matter expert. Attributes may relate to financial information associated with the home and/or homeowner, demographic information of the homeowner, home features and/or value, or any other metric that may be useful in determining a likelihood of a roof repair request being obtained for the home (Examiner asserts that the insurance adjustment estimate is equivalent to a processed insurance claim)).
As per claims 7 and 21, Armstrong teaches each of the limitations of claims 1 and 6, and 15 and 20 respectively.
In addition, Armstrong teaches:
wherein the plurality of features include one or more of damages, materials involved, labor costs or loss locations. (Paragraph Number [0056] teaches generation system 202 may process the information from the insurance carrier, or in combination with other data inputs 204, and generate a recommendation or other determination. For example, an insurance claim for a damaged roof may include photographs of the building, the roof, and the shingles. Using the information from the insurance carrier, generation system 202 may determine the type of materials required for repairing the damage, how much material is needed, how many workers would be required for the project, and/or other determinations. Following the analysis, generation system 202 may generate a recommendation or determination, providing the repair business with project bidding information allowing the repair business to bid on the repair project in a timely manner).
As per claims 8 and 22, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the construction schedule generation software layer determines at least one action to be performed for a construction project (Paragraph Number [0087] teaches upon a determination that an incoming project is likely to be accepted, step 310 may comprise system 200 scheduling workers or employees to begin traveling to the project location. For example, using weather forecasting information 208 as described above, system 200 may determine, with a pre-determined level of confidence, that an incoming stormfront will result in at least one repair project. Accordingly, system 200 may begin scheduling one or more employees or workers to travel to the area that is predicted to be hit by the stormfront, reducing the response time and allowing the workers to begin repairs in a timely manner).
As per claims 11 and 25, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the construction schedule generation software layer transmits the construction schedule and the expense estimates to an insurance carrier claims processing software application (Paragraph Number [0056] teaches using the information from the insurance carrier, generation system 202 may determine the type of materials required for repairing the damage, how much material is needed, how many workers would be required for the project, and/or other determinations. Following the analysis, generation system 202 may generate a recommendation or determination, providing the repair business with project bidding information allowing the repair business to bid on the repair project in a timely manner. Optionally or additionally, the generated recommendation or determination may also allow the repair business to begin ordering materials or scheduling workers to respond to the repair project without delay. Further, through communication between the insurance carrier and the repair business, the processing of claims and projects may occur in a more streamlined manner, reducing latency and improving efficiency).
As per claims 12 and 26, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
In addition, Armstrong teaches:
wherein the machine learning software layer tracks and processes adjustments made to the construction schedule or the expense estimates (Paragraph Number [0117] teaches an adjuster pane may display contact information for the insurance adjuster at the insurance company. A timeline pane may display a timeline of events for the repair project).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4, 9, 10, 18, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2023/368095 to Armstrong et al. (hereafter referred to as Armstrong) in view of U.S. Patent Application Publication Number 2022/0164741 to Mizikovsky et al. (hereafter referred to as Mizikovsky).
As per claims 4 and 18, Armstrong teaches each of the limitations of claims 1-3 and 15-17 respectively.
Armstrong teaches generation of construction schedules and expense estimates but does not explicitly teach utilizing at least one construction project rule, regulation, practice or pre-defined standard as described by the following citations from Mizikovsky:
wherein the data integration software layer receives data relating to at least one construction project rule, regulation, or practice (Paragraph Number [0048] teaches the system 100 provides an integrated technical solution to: automate trenches of rules-based work; schedule key business processes by making decisions on when to automatically instruct staff, subcontractors and/or suppliers to take subsequent steps; enforce business rules; reflect specific project requirements and processes; and compensate for loss of flexibility by speed and cost gains and redirection in efforts. The system 100 provides an ecosystem and a platform for sharing information and linking customers, builders, subcontractors, consultants and suppliers in an orderly efficient and coordinated manner through the processing modules 104 described below. Paragraph Number [0062] teaches the document library module 101 of FIG. 8 Document Library is configured so that: the latest document in a set is displayed first; it is possible to find mis-saved documents using numbers and letters strings; it has a business rules-based component requiring a certain critical document to be saved to allow job to progress to the next stage; and it controls access to specific documents and automatically shares documents with predetermined parties).
Both Armstrong and Mizikovsky are directed to insurance cost analysis. Armstrong discloses generation of construction schedules and expense estimates. Mizikovsky improves upon Armstrong by disclosing utilizing at least one construction project rule, regulation, practice or pre-defined standard. One of ordinary skill in the art would be motivated to further include utilizing at least one construction project rule, regulation, practice or pre-defined standard, to efficiently thresholds and patterns that are to be followed in making financial determination related to the pending construction project. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of generation of construction schedules and expense estimates in Armstrong to further utilize at least one construction project rule, regulation, practice or pre-defined standard in Mizikovsky, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 9 and 23, Armstrong teaches each of the limitations of claims 1 and 8, and 15 and 22 respectively.
Armstrong teaches generation of construction schedules and expense estimates but does not explicitly teach utilizing at least one construction project rule, regulation, practice or pre-defined standard as described by the following citations from Mizikovsky:
wherein the construction schedule generation software layer determines materials needed according to a pre-defined standard for completing the construction project (Paragraph Number [0048] teaches the system 100 provides an integrated technical solution to: automate trenches of rules-based work; schedule key business processes by making decisions on when to automatically instruct staff, subcontractors and/or suppliers to take subsequent steps; enforce business rules; reflect specific project requirements and processes; and compensate for loss of flexibility by speed and cost gains and redirection in efforts. The system 100 provides an ecosystem and a platform for sharing information and linking customers, builders, subcontractors, consultants and suppliers in an orderly efficient and coordinated manner through the processing modules 104 described below).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 4.
As per claims 10 and 24, the combination of Armstrong and Mizikovsky teaches each of the limitations of claims 1, 8, and 9, and 15, 22, and 23 respectively.
In addition, Armstrong teaches:
wherein the construction schedule generation software layer generates an interactive construction schedule indicating a real-time status of a construction project and remaining milestones (Paragraph Number [0088] teaches the software platform may comprise a dashboard comprising various panes, displays, windows, or other components for receiving and displaying information, which may be customizable to suit the needs of the user. For example, the dashboard for a repair business may comprise displays or components tailored for providing information about current projects, including the status of the projects, how many workers are dedicated to the project, the costs associated with the project, among other information. The information about current projects may aid the repair business in keeping track of the resources that are currently being expended, which may aid in how the repair business may accept projects in the future. The dashboard for a prospective client may likewise be tailored for providing an interface for the client to upload information about a prospective project and for selecting a repair business to perform the project. For example, the dashboard for the client may include an entry box or other means to provide the client an ability to upload pictures or videos of damage to the client’s home).
Claims 13, 14, 27, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2023/368095 to Armstrong et al. (hereafter referred to as Armstrong) in view of U.S. Patent Application Publication Number 2016/0035038 to Perkins (hereafter referred to as Perkins).
As per claims 13 and 27, Armstrong teaches each of the limitations of claims 1 and 15 respectively.
Armstrong teaches generation of construction schedules and expense estimates but does not explicitly teach comparing the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective as described by the following citations from Perkins:
wherein the machine learning software layer processes living expense data and compares the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective for an insured (Paragraph Number [0131] teaches as shown in FIG. 9, in a housing notes research screen 450 to begin the temporary housing search, at step the H/H subsystem determines a fair market evaluation 452 of the loss. To determine the fair market evaluation 452, first, at step 250, the H/H subsystem makes a rough estimate of the loss by determining an estimated monthly rental value. The rough estimate may be provided to the system by the H/H contractor, or determined from basic information regarding the loss, such as the type of dwelling, the number of bedrooms and bathrooms, the location of the loss, etc. Next, step 252, the H/H subsystem selects from a housing database 133 of nearby rentals those comparable units 444 that have the same type of dwelling, the same number of bedrooms, and the bathrooms. If there is no matches, or a number less than a predetermined amount, the H/H subsystem may back off the criteria until a predetermined number of rentals are selected. For example, the H/H subsystem may first drop the bathroom criteria, then drop the bedroom criteria, and then expand the location criteria until the predetermined number is reached. In an embodiment, the predetermined number is one to ensure a result. In other embodiments, the predetermined number is two to minimize the effect of outliers. In yet a further embodiment, the predetermined number is three to further minimize the effect of outliers. For all rentals selected, at step 254, the H/H subsystem may exclude those rentals not within one thousand dollars of the rough estimate, and average the monthly rental rate of the remaining selected rentals with the result being the fair market evaluation 452. (Examiner asserts that this exclusion and filtering would create a determination as to which temporary housing would be the most cost efficient)).
Both Armstrong and Perkins are directed to insurance cost analysis. Armstrong discloses generation of construction schedules and expense estimates. Perkins improves upon Armstrong by disclosing comparing the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective. One of ordinary skill in the art would be motivated to further include comparing the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective, to efficiently determine the most cost effective solution to a temporary housing need. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of generation of construction schedules and expense estimates in Armstrong to further utilize comparing the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective in Perkins, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 14 and 28, the combination of Armstrong and Perkins teaches each of the limitations of claims 1 and 13, and 15 and 27 respectively.
Armstrong teaches generation of construction schedules and expense estimates but does not explicitly teach comparing the living expense data with the construction schedule to determine whether at least one of long-term housing or a hotel would be most cost-effective as described by the following citations from Perkins:
wherein the living expense data is included in the expense estimates (Paragraph Number [0131] teaches as shown in FIG. 9, in a housing notes research screen 450 to begin the temporary housing search, at step the H/H subsystem determines a fair market evaluation 452 of the loss. To determine the fair market evaluation 452, first, at step 250, the H/H subsystem makes a rough estimate of the loss by determining an estimated monthly rental value. The rough estimate may be provided to the system by the H/H contractor, or determined from basic information regarding the loss, such as the type of dwelling, the number of bedrooms and bathrooms, the location of the loss, etc. Next, step 252, the H/H subsystem selects from a housing database 133 of nearby rentals those comparable units 444 that have the same type of dwelling, the same number of bedrooms, and the bathrooms. If there is no matches, or a number less than a predetermined amount, the H/H subsystem may back off the criteria until a predetermined number of rentals are selected. For example, the H/H subsystem may first drop the bathroom criteria, then drop the bedroom criteria, and then expand the location criteria until the predetermined number is reached. In an embodiment, the predetermined number is one to ensure a result. In other embodiments, the predetermined number is two to minimize the effect of outliers. In yet a further embodiment, the predetermined number is three to further minimize the effect of outliers. For all rentals selected, at step 254, the H/H subsystem may exclude those rentals not within one thousand dollars of the rough estimate, and average the monthly rental rate of the remaining selected rentals with the result being the fair market evaluation 452. (Examiner asserts that this exclusion and filtering would create a determination as to which temporary housing would be the most cost efficient)).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 13.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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