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 .
The instant application having application No. 18/951774 for FERCHICHI et al. for “COMPUTER-IMPLEMENTED METHOD OF ALERTING A PROPERTY MANAGER OF A ROOF POOLING ISSUE” filed November 19, 2024 has been examined.
Drawings
Drawings Figures 1-13 submitted on November 19, 2024 are in compliance with the provisions of 37 CFR 1.121(d).
Claim Rejections - 35 USC § 102/103
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.
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 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over by the Prior Art of GAWRON et al. (U.S. Publication No. 2023/0144043 A1) hereafter “Gawron” in view of the Prior Art of DU et al. (U.S. Publication No. 2023/0100515 A1) hereafter “Du”.
As to claim 1, Gawron disclose a computer-implemented method (a method 300 according to some embodiments is shown. In some embodiments, the method 300 may be performed and/or implemented by and/or otherwise associated with one or more specialized and/or specially-programmed computers [i.e. a computer-implemented method]), shown in Figure 3 and described in Paragraphs 0015 and 0028-0029, See also and The process 500 may comprise, for example, a statistical score analysis algorithm, Figure 5 and described in Paragraph 0049) of transmitting to a property manager device a pooling issue pertaining to a roof of a building (method 300 may comprise identifying (e.g., by the electronic processing device) a roof feature … pooling or ponding indications, described in Paragraphs 0035-0036, signal may be transmitted, for example, to one or more electronic devices of a user and/or customer, described in Paragraph 0040 [i.e. transmitting to a property manager device a pooling issue pertaining to a roof of a building]), the method comprising: providing image data including an image of the roof of the building (to identify an occurrence of an image/data pattern/value that matches predefined AI criteria indicative of a particular type of identifiable feature… Such features may comprise, but are not limited to, for example, cracks (e.g., defects in a roof membrane surface), drains (their existence, pooling or ponding indications [i.e. providing image data including an image of the roof of the building], described in Paragraphs 0035-0036); determining that water pooling is present in the image the roof of the building, including processing the image of the roof of the building using a trained AI algorithms (trained AI algorithms and/or modules may be employed, for example, to identify an occurrence of an image/data pattern/value that matches predefined AI criteria indicative of a particular type of identifiable feature… Such features may comprise, but are not limited to, for example, cracks (e.g., defects in a roof membrane surface), drains (their existence, pooling or ponding indications [i.e. determining that water pooling is present in the image the roof of the building, including processing the image of the roof of the building using a trained AI algorithms], described in Paragraphs 0035-0036); providing contextual data pertaining to at least one of weather, history, and drain obstruction associated to the determined water pooling; determining that the determined water pooling is indicative of the pooling issue (the score distribution 518 may represent a distribution of roof deterioration scores that may be computed and/or calculated via the method 300 and/or the process 400 of FIG. 3 and/or FIG. 4 herein. In some embodiments, the score distribution 518 may comprise a plurality of comparable scores 580-1, 580-2, 580-3, 580-4, 580-5 (e.g., comparable due to having at least one characteristic in common, such as being calculated from data acquired at the same time of year (seasonality), during similar weather conditions, times of day, etc.), such as a most-recent score 580-5 for a particular object/roof and a set of four (4) or more previous scores 580-1, 580-2, 580-3, 580-4 for the same object/roof., [i.e. providing contextual data pertaining to at least one of weather, history, and drain obstruction associated to the determined water pooling; determining that the determined water pooling is indicative of the pooling issue], described in Paragraph 0049), including validating a contextual criteria against the contextual data (the results of the progression analysis may be converted to a qualitative label such as “stable”, “deteriorating”, or “improved” by cross-referencing the numeric result with a matrix or table identifying applicable labels for different ranges in value [i.e. including validating a contextual criteria against the contextual data], described in Paragraph 0054); and transmitting to the property manager device contingent upon said determining that the determined water pooling is indicative of the pooling issue (the scoring of the feature may represent an increased likelihood of water damage due to roof ponding, described in Paragraph 0036, signal may be transmitted, for example, to one or more electronic devices of a user and/or customer, the signal including data identifying the overall defectiveness score, e.g., computed at 322, described in Paragraph 0040, [i.e. transmitting to the property manager device contingent upon said determining that the determined water pooling is indicative of the pooling issue]).
Gawron does not expressly disclose a trained convolutional neural network; and transmitting an alert.
Du discloses method comprising: using a convolution neural network-based (CNN-based) AI engine to analyze and determine an image of a building (described in Abstract and Paragraph 0007) and provide an alert to an end user (described in Paragraph 0265).
Thus, given the method of Gawron and having the teaching of Du disclosing a convolutional neural network; and transmitting an alert that is also well-known and conventional in the art, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to modify the method of Gawron by incorporating the teaching of Du in order to have a computer-implemented method of alerting a property manager of a pooling issue pertaining to a roof of a building, the method comprising: providing image data including an image of the roof of the building; determining that water pooling is present in the image the roof of the building, including processing the image of the roof of the building using a trained convolutional neural network; providing contextual data pertaining to at least one of weather, history, and drain obstruction associated to the determined water pooling; determining that the determined water pooling is indicative of the pooling issue, including validating a contextual criteria against the contextual data; and transmitting an alert to the property manager contingent upon said determining that the determined water pooling is indicative of the pooling issue, for the obvious advantages of stated by Du (Paragraph 0003).
As to claim 2, the combination of Gawron and Du as set forth above in claim 1, further Gawron discloses wherein said providing image data includes, using a camera (imaging device 106 [i.e. camera], imagery device 106 may comprise, for example, an iXM-RS150F/iXM-RS100F full frame ultra-high resolution aerial camera, shown in Figure 1 and described in Paragraphs 0015 and 0020), taking the image of the roof of the building (photographic, video, and/or other sensor data descriptive of the location 108 and/or the roof 150 [i.e. taking the image of the roof of the building], described in Paragraph 0020), transmitting the image of the roof of the building over a telecommunications network (Network 104 [i.e. telecommunications network], shown in Figure 1, the imaging device 106 may be in communication with (e.g., via the network 104) one or more of a controller device 110 and a memory device 140 (e.g., storing one or more AI modules 142), described in Paragraph 0015), and storing the image of the roof of the building in a non-transitory computer-readable memory (a memory device 140 [i.e. a non-transitory computer-readable memory], shown in Figure 1, the imaging device 106 may be in communication with (e.g., via the network 104) one or more of a controller device 110 and a memory device 140 (e.g., storing one or more AI modules 142), described in Paragraph 0015).
As to claim 3, the combination of Gawron and Du as set forth above in claim 1, further Gawron discloses wherein said image data includes metadata associated to the image of the roof of the building, the metadata including a timestamp of a moment in time at which the image of the roof of the building was taken, wherein said validating the contextual criteria against the contextual data factors in the moment in time at which the image of the roof of the building was taken via the timestamp (imagery/data itself by reading and/or analysis of metadata, described in Paragraph 0031 and imagery/data from a first time, for example, and may be compared to other overall defectiveness scores for the roof calculated based on imagery/data from other points in time, described in Paragraph 0039, see also Paragraph 0049).
As to claim 4, the combination of Gawron and Du as set forth above in claim 3, further having the disclosure of Gawron that discloses “a plurality of previous overall defectiveness scores for the roof that are based on a respective plurality of previous high-resolution images of the property taken at a plurality of previous times before the first time; computing, by the electronic processing device and based on a comparison of the plurality of previous overall defectiveness scores to the first overall defectiveness score, a measure of deviation of the first overall defectiveness score with respect to the plurality of previous overall defectiveness scores; comparing, by the electronic processing device, the measure of deviation to a stored deviation threshold; and classifying, by the electronic processing device and based on the comparing, the roof as being either (i) deteriorating, in the case that the measure of deviation exceeds or equals the deviation threshold or (ii) improving, in the case that the measure of deviation falls below the deviation threshold.” , described in Claim 1 and “wherein the plurality of previous overall defectiveness scores comprises at least four previous overall defectiveness scores that are based on a respective at least four previous high-resolution images of the property taken at least four previous times before the first time.”, described in Claim 7), it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the contextual criteria pertains contextual data dating from less than 4 days before the moment in time at which the image of the roof of the building was taken, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 5, the combination of Gawron and Du as set forth above in claim 3, further having the disclosure of Gawron that discloses “the score distribution 518 may represent a distribution of roof deterioration scores that may be computed and/or calculated via the method 300 and/or the process 400 of FIG. 3 and/or FIG. 4 herein. In some embodiments, the score distribution 518 may comprise a plurality of comparable scores 580-1, 580-2, 580-3, 580-4, 580-5 (e.g., comparable due to having at least one characteristic in common, such as being calculated from data acquired at the same time of year (seasonality), during similar weather conditions, times of day, etc.), such as a most-recent score 580-5 for a particular object/roof and a set of four (4) or more previous scores 580-1, 580-2, 580-3, 580-4 for the same object/roof.”, described in Paragraphs 0049 and 0054), it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the contextual data includes a weather history pertaining to a location at which the image of the roof of the building was taken, said validating the contextual criteria against the contextual data includes determining, from the weather history, an absence of rain at one or more moments in time preceding the moment in time at which the image of the roof of the building was taken, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 6, the combination of Gawron and Du as set forth above in claim 3, further having the disclosure of Gawron that discloses “imagery/data from a first time, for example, and may be compared to other overall defectiveness scores for the roof calculated based on imagery/data from other points in time”, described in Paragraph 0039 and “the score distribution 518 may represent a distribution of roof deterioration scores that may be computed and/or calculated via the method 300 and/or the process 400 of FIG. 3 and/or FIG. 4 herein. In some embodiments, the score distribution 518 may comprise a plurality of comparable scores 580-1, 580-2, 580-3, 580-4, 580-5 (e.g., comparable due to having at least one characteristic in common, such as being calculated from data acquired at the same time of year (seasonality), during similar weather conditions, times of day, etc.), such as a most-recent score 580-5 for a particular object/roof and a set of four (4) or more previous scores 580-1, 580-2, 580-3, 580-4 for the same object/roof.”, described in Paragraphs 0049 and 0054, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the image of the roof of the building is a first image of the roof of the building, the image data includes a plurality of additional images of the roof of the building taken at different moments in time associated to respective timestamps, the contextual data includes a history of previous determinations of whether or not water pooling was present in respective ones of the additional images of the roof, said validating the contextual criteria against the contextual data includes determining, from the history of previous determinations, an absence of rain at one or more of said different moments in times, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 7, the combination of Gawron and Du as set forth above in claim 3, further having the disclosure of Gawron that discloses “imagery/data from a first time, for example, and may be compared to other overall defectiveness scores for the roof calculated based on imagery/data from other points in time”, described in Paragraph 0039 and “the score distribution 518 may represent a distribution of roof deterioration scores that may be computed and/or calculated via the method 300 and/or the process 400 of FIG. 3 and/or FIG. 4 herein. In some embodiments, the score distribution 518 may comprise a plurality of comparable scores 580-1, 580-2, 580-3, 580-4, 580-5 (e.g., comparable due to having at least one characteristic in common, such as being calculated from data acquired at the same time of year (seasonality), during similar weather conditions, times of day, etc.), such as a most-recent score 580-5 for a particular object/roof and a set of four (4) or more previous scores 580-1, 580-2, 580-3, 580-4 for the same object/roof.”, described in Paragraphs 0049 and 0054, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the contextual data includes a weather history pertaining to a location at which the image of the roof of the building was taken, said validating the contextual criteria against the contextual data further includes determining, from the weather history, an absence of rain at one or more moments in time preceding the moment in time at which the image of the roof of the building was taken, said transmitting the alert to the property manager being contingent upon both said determining, from the history of previous determinations, the absence of rain at one or more of said different moments in time and determining, from the weather history, an absence of rain at one or more moments in time preceding the moment in time at which the image of the roof of the building was taken, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 8, the combination of Gawron and Du as set forth above in claim 1, further Gawron discloses wherein the contextual data includes weather data pertaining to a location of the roof of the building, and said determining that the determined water pooling is indicative of the pooling issue includes validating an absence of rain in the weather data (“additionally, photographs might not reveal hidden obstructions like debris or leaves blocking drains that affect water flow if they are submerged. As the AI model predominantly detects the presence of water without considering contextual factors, its inherent capability to differentiate between problematic and non-problematic scenarios is limited, resulting in a superficial understanding, and potentially leading to costly mistakes or unnecessary actions. FIG. 3 shows the number of alerts generated yearly in each location based on the different characteristics used in the decision process.”,[i.e. wherein the contextual data includes weather data pertaining to a location of the roof of the building, and said determining that the determined water pooling is indicative of the pooling issue includes validating an absence of rain in the weather data], described in Paragraph 0046).
As to claim 9, the combination of Gawron and Du as set forth above in claim 1, further having the disclosure of Gawron that discloses “the overall defectiveness score for the roof (quantitative and/or qualitative) may be utilized to compute a defectiveness trend for the roof/object. The overall defectiveness score for the roof may be calculated based on imagery/data from a first time, for example, and may be compared to other overall defectiveness scores for the roof calculated based on imagery/data from other points in time. In some embodiments, a plurality of previously calculated overall defectiveness scores for the roof may be utilized in conjunction with the currently-calculated overall defectiveness score for the roof to define a mean, standard deviation, and/or other statistical and/or mathematical metric descriptive of the series of scores. According to some embodiments, compared scores may be selected from a plurality of available scores based on one or more similar characteristics between the scoring events.”, described in Paragraph 0039 and further having the disclosure of Du that discloses “applying an image of the building to a convolution neural network-based (CNN-based) AI engine that has been trained to identify a first floor of a building from the image”, described in Paragraph 0007 and “if the response to the query in affirmative, the process proceeds to step S578, where an indicator indicating that the BFE is above the FFE is included in the graphic so as to alert an end user that there is an elevated risk of flood damage for that particular property ”, described in Paragraph 0265, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the image of the roof of the building is a first image of the roof of the building, the image data includes a plurality of additional images of the roof of the building taken at different moments in time associated to respective timestamps, further comprising determining that a drain is present in at least one of the additional images of the roof using the trained convolutional neural network, wherein said determining that the determined water pooling is indicative of the pooling issue including validating that the drain was determined as present in at least one of the additional images of the roof, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 10, the combination of Gawron and Du as set forth above in claim 1, further having the disclosure of Gawron that discloses “the overall defectiveness score for the roof (quantitative and/or qualitative) may be utilized to compute a defectiveness trend for the roof/object. The overall defectiveness score for the roof may be calculated based on imagery/data from a first time, for example, and may be compared to other overall defectiveness scores for the roof calculated based on imagery/data from other points in time. In some embodiments, a plurality of previously calculated overall defectiveness scores for the roof may be utilized in conjunction with the currently-calculated overall defectiveness score for the roof to define a mean, standard deviation, and/or other statistical and/or mathematical metric descriptive of the series of scores. According to some embodiments, compared scores may be selected from a plurality of available scores based on one or more similar characteristics between the scoring events.”, described in Paragraph 0039 and “a characteristic-based statistical approach may, for example, permit an understanding regarding whether the changes observed over time”, described in Paragraph 0039, “the scoring of the feature may represent an increased likelihood of water damage due to roof ponding, described in Paragraph 0036 and “signal may be transmitted, for example, to one or more electronic devices of a user and/or customer, the signal including data identifying the overall defectiveness score, e.g., computed at 322, described in Paragraph 0040, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have wherein the image of the roof of the building is a first image of the roof of the building, the image data includes a plurality of additional images of the roof of the building taken at different moments in time associated to respective timestamps, further comprising determining that debris is present in at least one of the additional images of the roof using the trained convolutional neural network, storing said determination that debris is present in association with the respective at least one of the additional images of the roof, wherein said determining that the determined water pooling is indicative of the pooling issue includes validating that the debris was determined as present in said at least one of the additional images of the roof, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
As to claim 11, the combination of Gawron and Du as set forth above in claim 9, further having the disclosure of Gawron that discloses “different characteristics of any particular feature may affect the score for the feature. In the case that the feature comprises an identified roof patch area, for example, the size, shape, pattern, data value, and/or location (e.g., relative to one or more other features, such as drains, seams, etc.) of the patch may govern the scoring. While a small roof patch may produce an elevated score, e.g., of six tenths (0.6) on a scale of zero (0) to one (1), for example, a larger roof patch and/or a roof patch within a predetermined distance to a roof drain may produce a less favorable score of, e.g., eight tenths (0.8).”, described in Paragraph 0036 and further having the disclosure of Du that discloses “applying an image of the building to a convolution neural network-based (CNN-based) AI engine that has been trained to identify a first floor of a building from the image”, described in Paragraph 0007 and “if the response to the query in affirmative, the process proceeds to step S578, where an indicator indicating that the BFE is above the FFE is included in the graphic so as to alert an end user that there is an elevated risk of flood damage for that particular property ”, described in Paragraph 0265, it would have been obvious to one of ordinary skill in the art at the time of effective filing date of the claimed invention to further modify the combination of Gawron and Du, in order to have the method further comprising determining that a drain is present in at least one of the additional images of the roof using the trained convolutional neural network, wherein said determining that the determined water pooling is indicative of the pooling issue further includes validating that the drain was determined as present in at least one of the additional images of the roof, which may be achieved thru routine experimentation with expected result without involving any inventive steps.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following cited arts are further to show the state of related art.
U.S. Publication No. 2022/0292822 A1 of SPLITTSTOESSER, discloses a damage assessment (DA) computing device for determining building damage may be provided. The DA computing device may train a machine learning damage model using historical damage data, identify a plurality of buildings that are susceptible to potential damage from an upcoming weather event, the plurality of buildings including the building, input data associated with a roof of the building to the trained damage model, receive a model output from the trained damage model, the model output including a damage status of the roof representing the predicted extent of damage to the roof, when the predicted extent of damage to the roof exceeds a threshold, automatically generate a claim initiation message including a link that, upon selection thereof, causes initiation an insurance claim for the roof based upon the model output and the parameters associated with the building, and/or transmit the claim initiation message to a user.
U.S. Publication No. 2020/0134573 A1 of VICKERS, discloses a system for identifying buildings that are damaged in a geographic area from a damaging weather event. Accessing weather data and identifying a date of the damaging weather event. Accessing geographic data for identifying the geographic area where the damaging weather event occurred. Accessing visual data of buildings where the damaging weather event occurred. Identifying an individual building that was damaged based on the visual data, geographic data and weather data.
U.S. Publication No. 2018/0336418 A1 of SPLITTSTOESSER, discloses a damage assessment (DA) computing device for determining building damage may be provided. The DA computing device may retrieve historical damage data associated with roof damage from a historical damage database, generate a damage model based upon the retrieved historical damage data, identify an building for a roof damage assessment, retrieve damage data associated with the building, compare the damage data associated with the roof to the damage model, and/or determine a damage status of the roof based upon the comparison.
U.S. Patent No. 12,056,611 B2 to DU et al, discloses a system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.
U.S. Patent No. 9,152,863 B1 to GRANT, discloses a system for remotely assessing the condition of a roof of a building is disclosed. The system may compare multiple pieces of image data of the roof, representing the roof at different moments in time, to determine if at least a portion of the roof has been repaired, replaced or damaged in the time between the pieces of image data. If the roof is determined to have been repaired, replaced or damaged, the system may calculate a date of repair, replacement or damage of the roof that corresponds to the date on which at least one of the pieces of image data was captured or created. In the case where the roof has been repaired or replaced, the system may calculate the age of the roof based on the date of repair or replacement, and subsequently, calculate the actual cash value (ACV) of the roof based on the roof age.
Correspondence
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/SISAY YACOB/ March 30, 2026
Primary Examiner, Art Unit 2686
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