DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Final Office Action in response to amended claims filed on July 6, 2026, the following has occurred: Claim 1 and 11 have been amended.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Double Patenting Rejection has been maintained in light of the amendment.
35 U.S.C. 101 rejection has been withdrawn in light of the amendment.
35 U.S.C. 103 rejection has been maintained in light of the amendment.
Priority
This application is a continuation application of U.S. patent application Ser. No. 18/309,387, filed Apr. 28, 2023, now U.S. Pat. No. 11,972,499, which is a continuation application of U.S. patent application Ser. No. 17/478,568, filed Sep. 17, 2021, now U.S. Pat. No. 11,663,684, which is a continuation application of and claims priority to U.S. patent application Ser. No. 15/815,029, filed Nov. 16, 2017, now U.S. Pat. No. 11,151,669.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 2, 4-12, and 14-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Application 18/309,387, now U.S. Pat. No. 11,972,499. Although the claims at issue are not identical, they are not patentably distinct from each other, because the subject matter claimed in the instant application is fully disclosed in the Pat. No. 11,151,669; Pat. No. 11,972,499; and U.S. Pat. No. 11,663,684; and is covered by the application since the present application and the patent are claiming common subject matter, as follow:
See below table for detail claim to claim comparison for U.S. Patent No. 11,972,499 (hereinafter, ‘499) and present application 18/635,775.
Present Application 18/635,775
U.S. Pat. No. 11,972,499
Comparison Analysis
1. A home cost analysis server comprising at least one processor in communication with at least one memory, wherein the at least one processor is programmed to:
train a machine-learning program based upon text- based metadata and at least one image associated with homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program, wherein the machine-learning program learns relationships between the text-based metadata and the images to identify features of the homes based at least in part upon on the training;
receive, via a graphical user interface (GUI) displayed on a user computing device of a user, user input including selection of a prospective home in a target geographic area; generate one or more constraints using retrieved data from a stored user profile for the user;
in response to the selection of the prospective home, retrieve text-based metadata and one or more images of the prospective home and apply the text-based metadata and the one or more images as inputs to the trained machine-learning program, which identifies at least one feature of the prospective home and provides the at least one identified feature as output;
using an identifier of the at least one output feature of the prospective home, perform a lookup in an external database storing historical insurance claim information to retrieve comparable historical additional costs from corresponding homes having a similar or comparable feature to the at least one output feature of the prospective home;
output, to the user, the GUI for the selected prospective home including an initial home cost, a selectable list of any additional home cost associated with each at least one output feature, a selectable option to apply the generated one or more constraints, and a graphical or text-based indicator having a first appearance reflecting the initial home cost;
receive user input indicating a selection of at least one additional home cost and of the option to apply the generated one or more constraints; and
in response to the user input, cause an update of the GUI for the selected prospective home such that the updated GUI includes (i) an anticipated home cost associated with the prospective home that incorporates the selected additional home cost and the initial home cost and (ii) the graphical or text-based indicator updated to have a second appearance reflecting whether the anticipated home cost complies with the generated one or more constraints.
1.A home cost analysis server comprising at least one processor in communication with at least one memory, wherein the at least one processor is programmed to:
train a machine learning program to identify features of homes from text-based metadata and at least one image associated with those homes, by inputting sample datasets of images and text-based metadata of homes into the machine learning program, to generate a machine-learned feature processing program;
receive user input from a user computing device associated with a prospective homebuyer, the user input including a prospective home in a target geographic area;
input text-based metadata and one or more images of the prospective home as inputs to the machine-learned feature processing program, which outputs at least one feature of the prospective home;
access an external database storing historical ancillary costs associated with homes in the target geographic area, wherein ancillary costs are based upon the geographic area and at least one feature of a respective home;
perform a lookup in the external database to retrieve comparable historical ancillary costs from associated homes having a similar or comparable feature to the at least one outputted feature of the prospective home;
display, at the user computing device, a first user interface including a selectable list of any ancillary home cost associated with each at least one outputted feature;
receive, from the user computing device, user input indicating a selection of at least one ancillary home cost; and
in response to the user input, display, at the user computing device, a second user interface including an anticipated home cost associated with the prospective home that includes the selected ancillary home cost.
Claim 1 shares the identical core steps of ‘499 patent, but different in following ways:
First, present claim recites the external database stores “historical insurance claim information” rather than the broader “historical ancillary costs.” Insurance claim costs are merely a well-known species of the genus “ancillary costs” associated with homeownership. Narrowing a claim from a genus to a known species does not render the claim patentably distinct, as it performs the exact same function for providing historical cost data from the ML identified feature in the exact same manner.
Next, present claim adds the step of generating constraints using “retrieved data from a stored user profile” and providing a “selectable option to apply the generated one or more constraints.” The ‘499 patent claims receiving input from a “prospective homebuyer” and calculating an anticipated home cost. It would have been obvious for a person of ordinary skilled in the art to store a homebuyer’s financial parameters in a “user profile” and use the parameters to generate “constraints” such as a budget limit. Utilizing user profiles to establish constraints or thresholds is a routine and conventional practice in software development and financial analysis tools.
2. The home cost analysis server of claim 1, wherein the historical insurance claim information comprises a plurality of historical insurance claims made on a respective plurality of insured homes, each historical insurance claim in the external database includes a respective claim value associated with a corresponding feature of the insured home.
8. The home cost analysis server of claim 1, wherein the at least one processor is further programmed to:
access a third external database storing historical direct maintenance costs associated with a plurality of insured homes;
analyze the text-based metadata and at least one image associated with the prospective home and the historical direct maintenance costs to determine one or more direct maintenance costs associated with the prospective home; and
display, at the user computing device, the first user interface, wherein the selectable list further includes the one or more direct maintenance costs associated with the prospective home.
9. The home cost analysis server of claim 8, wherein at least a portion of the direct maintenance costs are associated with insurance claims made on respective insured homes of the plurality of insured homes.
Patent ‘499 discloses a more specific and detail description of step for storing historical direct maintenance cost associated with a plurality of insured homes and the direct maintenance costs are associated with insurance claims made on respective insured homes, which encompasses the genus or broad limitation of the present application in claim 2.
It would have been obvious for the specie of ‘499 to include the genus as described in the present application.
4. The home cost analysis server of claim 1, wherein the GUI includes the selectable list containing a numeric value of each additional home cost associated with the respective at least one output feature and an excerpt of the text-based metadata or image describing or depicting the at least one output feature.
2. The home cost analysis server of claim 1, wherein the selectable list includes a numeric value of each ancillary home cost associated with the respective at least one outputted feature and an excerpt of the text-based metadata or image describing or depicting the at least one outputted feature.
The functional step of the two claims is identical. The only difference between present application and ‘499 is the lexicographical difference, “additional home cost” is representative of “ancillary home cost” in ‘499. It would have been obvious for the specie of ‘499 to include the genus as described in the present application.
5. The home cost analysis server of claim 1, wherein the GUI includes the selectable list containing a numeric value of each additional home cost associated with the respective at least one output feature and an expected time value associated with each additional home cost.
3. The home cost analysis server of claim 1, wherein the selectable list includes a numeric value of each ancillary home cost associated with the respective at least one outputted feature and an expected time value associated with each ancillary home cost.
The functional step of the two claims is identical. The only difference between present application and ‘499 is the lexicographical difference, “additional home cost” is representative of “ancillary home cost” in ‘499. It would have been obvious for the specie of ‘499 to include the genus as described in the present application.
6. The home cost analysis server of claim 5, wherein the expected time value is a repeating or periodic time value.
4. The home cost analysis server of claim 3, wherein the expected time value is a repeating or periodic time value.
The two claims are identical.
7. The home cost analysis server of claim 5, wherein the expected time value is a singular time value.
5. The home cost analysis server of claim 3, wherein the expected time value is a singular time value.
The two claims are identical.
8. The home cost analysis server of claim 7, wherein the singular time value is a predicted future date.
6. The home cost analysis server of claim 5, wherein the singular time value is a predicted future date.
The two claims are identical.
9. The home cost analysis server of claim 1, wherein the at least one processor is further programmed to access a second external database storing text-based metadata and images associated with homes available for purchase in the target geographic area to retrieve the text-based metadata and one or more images of the prospective home.
7. The home cost analysis server of claim 1, wherein the at least one processor is further programmed to access a second external database storing text-based metadata and images associated with homes available for purchase in the target geographic area to retrieve the text-based metadata and one or more images of the prospective home.
The two claims are identical.
10. The home cost analysis server of claim 1, wherein the at least one processor is further programmed to:
receive subsequent user input of a second prospective home;
analyze text-based metadata and one or more images associated with the second prospective home and the historical additional costs to determine one or more additional home costs associated the second prospective home; and
output, to the user, a comparison of the one or more additional home costs associated with the first prospective home and the one or more additional home costs associated with the second prospective home.
10. The home cost analysis server of claim 1, wherein the at least one processor is further programmed to:
receive subsequent user input of a second prospective home;
analyze text-based metadata and one or more images associated with the second prospective home and the historical ancillary costs to determine one or more ancillary home costs associated the second prospective home; and
display, at the user computing device, a third user interface including a comparison of the one or more ancillary home costs associated with the first prospective home and the one or more ancillary home costs associated with the second prospective home.
The functional step of the two claims is identical. The only difference between present application and ‘499 is the lexicographical difference, “additional home cost” is representative of “ancillary home cost” in ‘499. It would have been obvious for the specie of ‘499 to include the genus as described in the present application.
11. A computer-implemented method for identifying home costs, the method implemented using a home cost analysis server including one or more processors in communication with one or more memory devices, the method comprising: training a machine-learning program based upon text-based metadata and at least one image associated with homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program, wherein the machine-learning program learns relationships between the text-based metadata and the images to identify features of the homes based at least in part upon on the training; receiving, via a graphical user interface (GUI) displayed on a user computing device of a user, user input including selection of a prospective home in a target geographic area; generating one or more constraints using retrieved data from a stored user profile for the user; in response to the selection of the prospective home, retrieving text- based metadata and one or more images of the prospective home and applying the text-based metadata and the one or more images as inputs to the trained machine-learning program, which identifies at least one feature of the prospective home and provides the at least one identified feature as output; using an identifier of the at least one output feature of the prospective home, performing a lookup in an external database storing historical insurance claim information to retrieve comparable historical additional costs from corresponding homes having a similar or comparable feature to the at least one output feature of the prospective home; outputting, to the user, the GUI for the selected prospective home including an initial home cost, a selectable list of any additional home cost associated with each at least one output feature, a selectable option to apply the generated one or more constraints, and a graphical or text-based indicator having a first appearance reflecting the initial home cost; receiving user input indicating a selection of at least one additional home cost and of the option to apply the generated one or more constraints; and in response to the user input, causing an update of the GUI for the selected prospective home such that the updated GUI includes (i) an anticipated home cost associated with the prospective home that incorporates the selected additional home cost and the initial home cost and (ii) the graphical or text-based indicator updated to have a second appearance reflecting whether the anticipated home cost complies with the generated one or more constraints.
11. A computer-implemented method for identifying home costs, the method implemented using a home cost analysis server including one or more processors in communication with one or more memory devices, the method comprising:
training a machine learning program to identify features of homes from text-based metadata and at least one image associated with those homes, by inputting sample datasets of images and text-based metadata of homes into the machine learning program, to generate a machine-learned feature processing program;
receiving user input from a user computing device associated with a prospective homebuyer, the user input including a prospective home in a target geographic area;
inputting text-based metadata and one or more images of the prospective home as inputs to the machine-learned feature processing program, which outputs at least one feature of the prospective home;
accessing an external database storing historical ancillary costs associated with homes in the target geographic area, wherein ancillary costs are based upon the geographic area and at least one feature of a respective home;
performing a lookup in the external database to retrieve comparable historical ancillary costs from associated homes having a similar or comparable feature to the at least one outputted feature of the prospective home;
displaying, at the user computing device, a first user interface including a selectable list of any ancillary home cost associated with each at least one outputted feature;
receiving, from the user computing device, user input indicating a selection of at least one ancillary home cost; and
in response to the user input, displaying, at the user computing device, a second user interface including an anticipated home cost associated with the prospective home that includes the selected ancillary home cost.
The reasoning of claim 1 is applied here.
12. The computer-implemented method of claim 11, wherein the historical insurance claim information includes a plurality of historical insurance claims made on a respective plurality of insured homes, each historical insurance claim in the external database includes a respective claim value associated with a corresponding feature of the insured home.
18. The computer-implemented method of claim 11, further comprising
accessing a third external database storing historical direct maintenance costs associated with a plurality of insured homes;
analyzing the text-based metadata and at least one image associated with the prospective home and the historical direct maintenance costs to determine one or more direct maintenance costs associated with the prospective home; and
displaying, at the user computing device, the first user interface, wherein the selectable list further includes the one or more direct maintenance costs associated with the prospective home.
19. The computer-implemented method of claim 18, wherein at least a portion of the direct maintenance costs are associated with insurance claims made on respective insured homes of the plurality of insured homes.
The reasoning of claim 2 is applied here.
14. The computer-implemented method of claim 11, wherein outputting the GUI including the selectable list comprises outputting the GUI including the selectable list that contains a numeric value of each additional home cost associated with the respective at least one output feature and an excerpt of the text-based metadata or image describing or depicting the at least one output feature.
12. The computer-implemented method of claim 11, wherein displaying the first user interface comprises displaying the first user interface including the selectable list that includes a numeric value of each ancillary home cost associated with the respective at least one outputted feature and an excerpt of the text-based metadata or image describing or depicting the at least one outputted feature.
The reasoning of claim 4 is applied here.
15. The computer-implemented method of claim 11, wherein outputting the GUI including the selectable list comprises outputting the GUI including the selectable list that contains a numeric value of each additional home cost associated with the respective at least one output feature and an expected time value associated with each additional home cost.
13. The computer-implemented method of claim 11, wherein displaying the first user interface comprises displaying the first user interface including the selectable list that includes a numeric value of each ancillary home cost associated with the respective at least one outputted feature and an expected time value associated with each ancillary home cost.
The reasoning of claim 5 is applied here.
16. The computer-implemented method of claim 15, wherein the expected time value is a repeating or periodic time value.
14. The computer-implemented method of claim 13, wherein the expected time value is a repeating or periodic time value.
The two claims are identical.
17. The computer-implemented method of claim 15, wherein the expected time value is a singular time value.
15. The computer-implemented method of claim 13, wherein the expected time value is a singular time value.
The two claims are identical.
18. The computer-implemented method of claim 17, wherein the singular time value is a predicted future date.
16. The computer-implemented method of claim 15, wherein the singular time value is a predicted future date.
The two claims are identical.
19. The computer-implemented method of claim 11, further comprising accessing a second external database storing text-based metadata and images associated with homes available for purchase in the target geographic area to retrieve the text-based metadata and one or more images of the prospective home.
17. The computer-implemented method of claim 11, further comprising accessing a second external database storing text-based metadata and images associated with homes available for purchase in the target geographic area to retrieve the text-based metadata and one or more images of the prospective home.
Same reasoning as claim 9 is applied here.
20. The computer-implemented method of claim 11, further comprising:
receiving subsequent user input of a second prospective home;
analyzing text-based metadata and one or more images associated with the second prospective home and the historical additional costs to determine one or more additional home costs associated the second prospective home; and
outputting, to the user, a comparison of the one or more additional home costs associated with the first prospective home and the one or more additional home costs associated with the second prospective home.
20. The computer-implemented method of claim 11, further comprising:
receiving subsequent user input of a second prospective home;
analyzing text-based metadata and one or more images associated with the second prospective home and the historical ancillary costs to determine one or more ancillary home costs associated the second prospective home; and
displaying, at the user computing device, a third user interface including a comparison of the one or more ancillary home costs associated with the first prospective home and the one or more ancillary home costs associated with the second prospective home.
Same reasoning as claim 10 is applied here.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-9, 11, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rawat et al. (US 10430902 B1) in view of Seitomer et al. (US 20100042442 A1) and further in view of Gokhale et al (US 20160335695 A1), and further in view of Binder (US 20160012510 A1).
Claims 1 and 11, Rawat discloses a home cost analysis server and computer-implemented method (claim 19) for identifying home costs, comprising at least one processor in communication with at least one memory (Col. 3 Ln. 44-45: “Real estate server 106 includes processor 108, interface 110, and database 112. Processor 108”), wherein the at least one processor is programmed to:
train a machine-learning program based upon text-based metadata and at least one image associated with homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program, wherein the machine-learning program learns relationships between the text-based metadata and the images to identify features of the homes based at least in part upon on the training (Rawat, Col. 6, ln. 3-11: “a user interface allowing a user to indicate whether the tags correctly describe the image. In the example shown, image tag confirmation screen 400 comprises an image of a kitchen along with the tags “kitchen” and “white cabinets,” and an interface for indicating whether the tags are correct. In some embodiments, information provided to the image tag confirmation screen is provided to the real estate server for training an image tagging algorithm.” The input of tags is representative of text-based metadata and provided with image to train image tagging algorithm, which is representative of machine learning program. More details provided in Col. 9 ln. 1-5: “the process of FIG. 8 comprises a process for training an algorithm to determine one or more attributes from image data”; Col. 9, ln. 13-19, “In 802, attributes for a set of images of the collection of images are manually identified…. In 804, a machine learning algorithm is trained using the manually identified attributes.” Col. 9 ln. 22-31“as part of training, the machine learning algorithm may convert images into a different set of representations (e.g., visual representations) including edge based features, color based features, high level abstractions (e.g., deep convolution networks), depth information, other real estate property attributes, real estate property description, user interaction logs, order of the images, or any other representations. In some embodiments, an image database that has associated metadata is used to train the machine learning algorithm (e.g., Imagenet).”);
receive, via a graphical user interface (GUI) displayed on a user computing device of a user, user input including selection of a prospective home in a target geographic area; (Rawat: Col. 6 Ln. 17-50: “Real estate property search window 500 comprises a user interface for entering a real estate property search. In the example shown, real estate property search window 500 comprises fields for entering property data (e.g., number of bedrooms, number of bathrooms, square footage range, price range, zip code, etc.). Real estate property search window 500 additionally comprises attribute selectors. In some embodiments, attribute selectors comprise menu selectors for selecting property attributes from the set of all property attributes. In some embodiments, the real estate property search comprises a search for a specific attribute. In various embodiments, real estate properties are searched by location attributes, room type attributes, room attributes, house type attributes, or any other appropriate attributes. In some embodiments, when a property attribute is selected using a property attribute selector, the user interface of real estate property search window 500 creates a new property attribute selector for selecting an additional property attribute if desired. Real estate property search window 500 additionally comprises a search button for executing a real estate property search according to the entered property data and property attributes. In some embodiments, a search is performed using a free form text query (e.g., as entered in a search field text entry box). In some embodiments, a search is performed within a real estate property description. In some embodiments, properties identified using a search are displayed based on the current location of a user, where the current location is determined using geolocation services available on the user's phone or tablet.” disclosing user can input text or select property attribute for desired (prospective) home in a target geographic location);
generate one or more constraints using retrieved data (Col. 10, Ln. 51-54: “image page includes user selected organization, filtering, sizing, ranking and searching of images, or any other appropriate user customization”. which teaches user-specific customization of results);
in response to the selection of the prospective home, retrieve text-based metadata and one or more images of the prospective home and apply the text-based metadata and the one or more images as inputs to the trained machine-learning program, which identifies at least one feature of the prospective home and provides the at least one identified feature as output (Rawat, Col. 9 ln. 1-7: “FIG. 8 is a flow diagram illustrating an embodiment of a process for training an algorithm. In some embodiments, the process of FIG. 8 comprises a process for training an algorithm to determine one or more attributes from image data. In some embodiments, after training using the process of FIG. 8, the algorithm is capable of implementing 702 of FIG. 7.” Continue in Col. 7 ln. 11-47 “tags are automatically determined in an image using image processing and the tags are used as attributes associated with a real estate property.” and Fig. 7; with Col. 6 Ln. 40-45: “a real estate property search according to the entered property data and property attributes. In some embodiments, a search is performed using a free form text query (e.g., as entered in a search field text entry box)”; and Col. 6 ln. 64-65: “image page 600 additionally comprises the text attributes used to create the page.” disclosing receiving image and associated tag/text (i.e., text-based metadata) attribute query for a search to provide for determining images attributes (feature) and a display of database entry of real estate property and associated attribute (feature));
using an identifier of the at least one output feature of the prospective home (Fig. 2 and Col. 4 Ln. 10 – Col. 5 Ln. 52 disclosing providing identifier for output features (i.e. image-determined attributes of the real estate),
output, to the user, the GUI for the selected prospective home including an initial home cost, and a graphical or text-based indicator having a first appearance reflecting the initial home cost (Col. 6 Ln. 21-26, “Real estate property search window 500 comprises a user interface for entering a real estate property search. In the example shown, real estate property search window 500 comprises fields for entering property data (e.g., number of bedrooms, number of bathrooms, square footage range, price range, zip code, etc.)” Col. 7 Ln. 42-47 and Col. 10 Ln. 14-21 disclosing the display of full detail of real estate property to the user in response to search, which includes price (i.e., initial home cost));
in response to the user input, cause an update of the GUI for the selected prospective home such that the updated GUI includes the initial home cost (Col. 7 Ln. 42-47 and Col. 10 Ln. 14-21 disclosing the display of full detail of real estate property to the user in response to search, which includes price (i.e., initial home cost)).
Rawat discloses the above-mentioned limitations of receiving and updating real estate database with images and associated metadata.
However, Rawat does not expressly teach,
generate one or more constraints using retrieved data from a stored user profile for the user;
access an external database storing historical insurance claim information including historical additional costs associated with homes in the target geographic area, wherein the historical additional costs are related to at least one feature of a respective home;
perform a lookup in an external database storing historical insurance claim information to retrieve comparable historical additional costs from corresponding homes having a similar or comparable feature to the at least one output feature of the prospective home;
output, to the user, the GUI for the selected prospective home including a selectable list of any additional home cost associated with each at least one output feature, a selectable option to apply the generated one or more constraints,
receive user input indicating a selection of at least one additional home cost and of the option to apply the generated one or more constraints;
in response to the user input, cause an update of the GUI for the selected prospective home such that the updated GUI includes (i) an anticipated home cost associated with the prospective home that incorporates the selected additional home cost and (ii) the graphical or text-based indicator updated to have a second appearance reflecting whether the anticipated home cost complies with the generated one or more constraints.
Nonetheless, Seitomer is in the same field of real estate evaluation, which specifically teaches,
access an external database storing historical insurance claim information including historical additional costs associated with homes in the target geographic area, wherein the historical additional costs are related to at least one feature of a respective home (Fig.4 and para. [0034] teaches the entering of address and zip code which is representative of searching for homes in target geographic area. Also entering square footage and year build and click checkbox of terrace or balcony is selection of features for the respective home. In para. [0035] teaches accessing database based on customer’s data entered to identify historical cost/values to estimate the total replacement cost (TRC) of homes. Also, in para. [0036], broker enters the number of each type of room in the home by selecting the appropriate number from the drop down menus (530) (e.g., number of bedrooms, bathrooms, family rooms, etc.) which is representative of selection of features. In para. [0037] and [0039] the calculated includes replacement cost of the additions and alterations, the contents, the terrace, premium of insurance policy (i.e., insurance claim information cost) and total replacement cost);
perform a lookup in an external database storing historical insurance claim information to retrieve comparable historical additional costs from corresponding homes having a similar or comparable feature to the at least one output feature of the prospective home (para. [0006], “accessing replacement costs of the different characteristics of the customer's home from at least one matrix stored in a replacement cost database based on a set of criteria. In addition, the method further comprises calculating an estimated replacement cost of the customer's home from the replacement costs of the different characteristics of the customer's home and generating an initial insurance premium and the initial insurance coverage”. Para. [0022], “categories of values for home renovations are stored in tables or matrices, as shown in Table 1-7, as part of a database, for later access by a home value estimator software application.” Fig. 8 and para. [0040] teaching the information gathered for home value estimator application is stored in database to be retrieved for customer, broker or other users to retrieve as comparable historical additional cost (e.g., A&A, Content, Terrance, and total replacement cost) with comparable feature (e.g., address, zip, living area, year built). Further see Example 1 in para. [0048]-[0050] including Table 1-8 which discloses comparable historical additional costs (e.g., cost of A&A, cost of Terrace, replacement cost of content, total replacement cost, and cost per policy holder) having similar or comparable features (for example, changing cost with different sqft and quality);
output, to a user, a selectable list of any additional home cost associated with each at least one output feature (Fig. 5 and [0036]);
receive user input indicating a selection of at least one additional home cost (Fig. 5 and para. [0031], [0032] [0036] and Claim 10 teaching the receiving of user input indicating selection of replacement cost associated with characteristic of home); and
in response to the user input, cause an update of the GUI for the selected prospective home such that the updated GUI includes (i) an anticipated home cost associated with the prospective home that incorporates the selected additional home cost (para. [0032], [0036], [0039] and Figs. 5 and 7 disclosing in response to user input, estimated (anticipated) total replacement cost of the home is calculated).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of updating real estate database property database of Rawat to include the features of accessing additional cost associated with home searching and providing the options for user to select and compare additional home cost for real estate evaluation as taught by Seitomer for the motivation of better assess and evaluate the actual cost of owning a property within the buyer’s budget to avoid the potential of defaulting on property from unable to pay additional expenses. Further, the claimed invention is merely a combination of old elements in a similar real estate evaluation field of endeavor. In such combination each element merely would have performed the same real estate evaluation related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Seitomer, the results of the combination were predictable (See MPEP 2143 A).
While Rawat teaches user-specific customization of results (Col. 10, Ln. 51-54: “image page includes user selected organization, filtering, sizing, ranking and searching of images, or any other appropriate user customization”. Rawat is not explicit on generating constraints from a profile.
The combination fails to expressly teach the limitations (italic emphasis):
generate one or more constraints using retrieved data from a stored user profile for the user;
a selectable option to apply the generated one or more constraints;
and of the option to apply the generated one or more constraints;
and (ii) the graphical or text-based indicator updated to have a second appearance reflecting whether the anticipated home cost complies with the generated one or more constraints.
Gokhale is analogous in the field of endeavor, which specifically teaches,
generate one or more constraints using retrieved data from a stored user profile for the user (Para. [0007], “determines residency affordability (e.g., home buyer affordability) based on down payment and/or monthly mortgage budget (e.g., instead of listing price).” In para. [0021] and [0031] teaches the system finds all properties which a user can afford based on the store user customizations and financial parameters);
a selectable option to apply the generated one or more constraints (para. [0050], [0051], [0076], [0077] teaching the selectable preferences (constraints) received from the user);
and of the option to apply the generated one or more constraints (para. [0050], [0051], [0076], [0077] teaching the selectable preferences (constraints) received from the user);
and (ii) the graphical or text-based indicator updated to have a second appearance reflecting whether the anticipated home cost complies with the generated one or more constraints (para. [0047], “process above is described as receiving data iteratively from the user, where the search criteria data is received at particular steps throughout the process and/or incorporated into the search in real time.” Gokhale teaches the dynamic updated of the GUI as preferences or search criteria (i.e., constraints) are added and the system updates the search result in real time).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of updating real estate database property database of Rawat to include the features of providing additional selectable preferences options from the user as taught by Gokhale for the motivation of providing a “fast and efficient property search tool that simplifies and improves the home search (e.g., buying, renting) experience, optimizes the search for increased speed, and incorporates fundamental parameters (e.g., commute time, total cost of ownership, school ratings, safety ratings, etc.)” (Gokhale para. [0006]).
Claims 4 and 14, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 1 and the method of claim 11.
Seitomer further teaches, wherein the GUI includes the selectable list containing a numeric value of each additional home cost associated with the respective at least one output feature and an excerpt of the text-based metadata or image describing or depicting the at least one output feature (Seitomer: Fig. 5 and [0036] the selectable list includes a numeric value of each additional home cost associated with at least one output feature and an excerpt of the text-based metadata of the feature). Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the selectable list includes is supposed to be is directed towards descriptive language that fails to further limit or alter how the steps/functions of the invention are performed or limit or alter the structure of the invention. The Examiner asserts that the claimed invention fails to establish the criticality of what the selectable list includes as the claimed invention does not explicitly recite how an analysis is affected by the information that is included/provided. The Examiner asserts that regardless of the selectable list includes is included/presented, the invention, as claimed, would be performed the same and have the same end result.
The rationales to modify/combine the teachings of Rawat with/and the teachings of Seitomer are presented in the examining of independent claims 1 and 11 and incorporated herein.
Claims 5 and 15, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 1 and the method of claim 11.
wherein the GUI includes the selectable list containing a numeric value of each additional home cost associated with the respective at least one output feature (Seitomer: Fig. 5 and [0036]) and an expected time value associated with each additional home cost (Seitomer: Fig. 8 and [0040] includes the date calculated for additional home cost). Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the selectable list includes is supposed to be is directed towards descriptive language that fails to further limit or alter how the steps/functions of the invention are performed or limit or alter the structure of the invention. The Examiner asserts that the claimed invention fails to establish the criticality of what the selectable list includes as the claimed invention does not explicitly recite how an analysis is affected by the information that is included/provided. The Examiner asserts that regardless of the selectable list includes is included/presented, the invention, as claimed, would be performed the same and have the same end result. Seitomer further teaches
The rationales to modify/combine the teachings of Rawat with/and the teachings of Seitomer are presented in the examining of independent claims 1 and 11 and incorporated herein.
Claims 6 and 16, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 5 and the method of claim 15. Seitomer further teaches
wherein the expected time value is a repeating or periodic time value (Seitomer: para. [0025], [0029], and [0045] teaching the cost is updated periodically (monthly, quarterly, annually, etc.)). Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the singular time value is supposed to be is directed towards descriptive language that fails to further limit or alter how the steps/functions of the invention are performed or limit or alter the structure of the invention. The Examiner asserts that the claimed invention fails to establish the criticality of expected time value as the claimed invention does not explicitly recite how an analysis is affected by the expected time value that is included/provided. The Examiner asserts that regardless of the expected time value is being included/presented, the invention, as claimed, would be performed the same and have the same end result.
Claims 7 and 17, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 5 and the method of claim 15. Seitomer further teaches
wherein the expected time value is a singular time value (Seitomer: Fig. 8 and [0040] includes the date calculated for additional home cost). Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the singular time value is supposed to be is directed towards descriptive language that fails to further limit or alter how the steps/functions of the invention are performed or limit or alter the structure of the invention. The Examiner asserts that the claimed invention fails to establish the criticality of expected time value as the claimed invention does not explicitly recite how an analysis is affected by the expected time value that is included/provided. The Examiner asserts that regardless of the expected time value is being included/presented, the invention, as claimed, would be performed the same and have the same end result.
Claims 8 and 18, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 7 and the method of claim 17. Seitomer further teaches
wherein the singular time value is a predicted future date (Seitomer: para. [0045], “insurance company determines the initial premium and coverage by reference to the loss event frequency rate for insuring a home. The loss event frequency rate includes events such as loss of home due to fire, natural disaster, or some other loss event in a particular geographic location over a time period (e.g., month, year, etc.).” teaching the determined policy cost is associated with premium at the geographic location over a time period, which is the policy is for predicted future date.). Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the singular time value is supposed to be is directed towards descriptive language that fails to further limit or alter how the steps/functions of the invention are performed or limit or alter the structure of the invention. The Examiner asserts that the claimed invention fails to establish the criticality of singular time value as the claimed invention does not explicitly recite how an analysis is affected by the singular time value that is included/provided. The Examiner asserts that regardless of the singular time value is being included/presented, the invention, as claimed, would be performed the same and have the same end result.
Claims 9 and 19, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 1 and the method of claim 11. Rawat further discloses
wherein the at least one processor is further programmed to access a second external database storing text-based metadata and images associated with homes available for purchase in the target geographic area to retrieve the text-based metadata and one or more images of the prospective home (Rawat, Col. 9 ln. 1-7: “FIG. 8 is a flow diagram illustrating an embodiment of a process for training an algorithm. In some embodiments, the process of FIG. 8 comprises a process for training an algorithm to determine one or more attributes from image data. In some embodiments, after training using the process of FIG. 8, the algorithm is capable of implementing 702 of FIG. 7.” Continue in Col. 7 ln. 11-47 and Fig. 7; with Col. 6 Ln. 40-45: “a real estate property search according to the entered property data and property attributes. In some embodiments, a search is performed using a free form text query (e.g., as entered in a search field text entry box)”; and Col. 6 ln. 64-65: “image page 600 additionally comprises the text attributes used to create the page.” disclosing receiving image and associated tag/text (i.e., text-based metadata) attribute query for a search to provide for determining images attributes (feature) and a display of database entry of real estate property and associated attribute (feature)).
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Rawat et al. (US 10430902 B1), in view of Seitomer et al. (US 20100042442 A1), in view of Gokhale et al (US 20160335695 A1) and further in view of Gross (US 20160048934 A1).
Claims 2 and 12, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 1 and the method of claim 11.
However, the combination fails to teach, wherein the historical insurance claim information comprises a plurality of historical insurance claims made on respective plurality of insured homes, each historical insurance claim in the external database includes a respective claim value associated with a corresponding feature of the insured home.
Nonetheless, Gross is in similar field of real estate evaluation, which specifically teaches,
wherein the historical insurance claim information comprises a plurality of historical insurance claims made on a respective plurality of insured homes, each historical insurance claim in the external database includes a respective claim value associated with a corresponding feature of the insured home (Gross, para. [0592], “1) Insurance: policy premiums, risk assessments, etc., can be based on an evaluation of an upkeep/maintenance evidenced for a particular property; in this respect correlations may be developed between property condition ratings, occupancy estimates and number of claims filed, type of claim, severity, etc. For example a property insurer is likely to be interested in knowing if a building is vacant and thus more likely to be vandalized or have a higher risk of arson, etc. Other potential hazards (trees that are too close or overgrown, dilapidated ancillary structures adjacent to a structure, undesirable and dangerous fixtures (trampolines etc.) can be identified by insurers and used to adjust premiums on a structure by structure basis. Other similar uses will be apparent to skilled artisans;”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of updating real estate database property database of Rawat to include wherein the historical insurance claim information comprises a plurality of historical insurance claims made on a respective plurality of insured homes, each historical insurance claim in the external database includes a respective claim value associated with a corresponding feature of the insured home, as taught by Gross, for the motivation of providing a tool to quickly and accurately assess and identify the health or quality of the property (para. [0007]). Further, the claimed invention is merely a combination of old elements in a similar real estate evaluation field of endeavor. In such combination each element merely would have performed the same real estate evaluation related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Gross, the results of the combination were predictable (See MPEP 2143 A).
Claims 3 and 13, the combination of Rawat, Seitomer, Gokhale and Gross make obvious of the server of claim 2 and the method of claim 12.
Seitomer further teaches, wherein the historical insurance claim information further comprises geographical locations corresponding the insured homes, wherein the historical additional costs are further associated with the geographic location of the respective home (Seitomer: Fig.4 and para. [0034] teaches the entering of address and zip code which is representative of searching for homes in target geographic area)
The rationales to modify/combine the teachings of Rawat with/and the teachings of Seitomer are presented in the examining of independent claims 1 and 11 and incorporated herein.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rawat et al. (US 10430902 B1), in view of Seitomer et al. (US 20100042442 A1), in view of Gokhale et al (US 20160335695 A1) and further in view of Binder (US 20160012510 A1).
Claims 10 and 20, the combination of Rawat, Seitomer, and Gokhale make obvious of the server of claim 1 and the method of claim 11.
The combination of Rawat and Seitomer in claims 1 and 11, makes obvious and teaches, receive user input of a prospective home (Rawat: Col. 6 Ln. 17-50);
analyze text-based metadata and one or more images associated with the prospective home (Rawat, Col. 9 ln. 1-7) and the historical additional costs to determine one or more additional home costs (Seitomer: Fig.4 and para. [0034], [0036], [0037] and [0039]).
However, the combination fails to expressly teach the steps of receiving user input and analyzing text-based metadata and one or more images for a second time for a second prospective home and comparing the results.
Specifically, the combination fails to expressly teach the limitations (italic emphasis included):
receive subsequent user input of a second prospective home;
analyze text-based metadata and one or more images associated with the second prospective home and the historical additional costs to determine one or more additional home costs associated the second prospective home; and
output, to the user, a comparison of the one or more additional home costs associated with the first prospective home and the one or more additional home costs associated with the second prospective home.
However it would have been obvious to one of ordinary skill in the art at the time the invention was made to receive user input and analyze text-based metadata and one or more images associated with the prospective home and the historical additional costs to determine one or more additional home costs associated the prospective home but also to perform the same step a second (another) time for a second prospective home on the same system/device, since it has been held that mere duplication of the essential working parts of the system/device involves the same routine skill in the art.
(In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960) (Claims at issue were directed to a water-tight masonry structure wherein a water seal of flexible material fills the joints which form between adjacent pours of concrete. The claimed water seal has a “web” which lies in the joint, and a plurality of “ribs” projecting outwardly from each side of the web into one of the adjacent concrete slabs. The prior art disclosed a flexible water stop for preventing passage of water between masses of concrete in the shape of a plus sign (+). Although the reference did not disclose a plurality of ribs, the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.)
Still, the combination fails to teach,
output, to the user, a comparison of the one or more additional home costs associated with the first prospective home and the one or more additional home costs associated with the second prospective home.
However, Binder, which id directed to analogous field of an improved system and method for analyzing multiple real estate properties, specifically teaches:
output, to the user, a comparison of the one or more additional home costs associated with the first prospective home and the one or more additional home costs associated with the second prospective home (Figs. 5-6, para. [0012], [0023]-[0027], [0030], [0037]-[0048]).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of updating real estate database property database of Rawat to include the features of comparing additional home costs associated with two prospective homes searched by the user as taught by Binder for the motivation and benefit of providing an improved and effective system and technique to analyze a large number of available real estate properties based on multiple criteria side by side for user to quickly distinguish the difference and quickly make decision on which property/home is better in value (Binder, para. [0006]).
Response to Remarks
Double Patenting Rejection:
The Applicant requests the rejection be held in abeyance. The Examiner notes the double patenting rejection held in abeyance is not permitted as indicated in MPEP 804(I)(1), “A complete response to a nonstatutory double patenting (NSDP) rejection is either a reply by applicant showing that the claims subject to the rejection are patentably distinct from the reference claims, or the filing of a terminal disclaimer in accordance with 37 CFR 1.321 in the pending application(s) with a reply to the Office action (see MPEP § 1490 for a discussion of terminal disclaimers). Such a response is required even when the nonstatutory double patenting rejection is provisional.
As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only compliance with objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated. Replies with an omission should be treated as provided in MPEP § 714.03. Therefore, an application must not be allowed unless the required compliant terminal disclaimer(s) is/are filed and/or the withdrawal of the nonstatutory double patenting rejection(s) is made of record by the examiner. See MPEP § 804.02, subsection VI, for filing terminal disclaimers required to overcome nonstatutory double patenting rejections in applications filed on or after June 8, 1995.” (Bold emphasis added)
35 U.S.C. 101 Rejection:
Notwithstanding applicant’s remarks, the claims have been amended include the limitation for the machine learning program “learns relationships between the text-based metadata and the images to identify features,” the Applicant has further tied the claim to a specific, technical computer implementation of training an AI model to correlate visual pixel data with text strings. Coupling the specific database lookup of insurance claims and the dynamic GUI state-change, the claim successfully integrates the abstract idea of identifying home costs and budgeting, into a practical application.
Thus, the 101 rejection is withdrawn.
35 U.S.C. 103 Rejection:
The Applicant’s remarks have been fully considered, however, they are found unpersuasive.
On pages 11-12, Applicant argues that no combination of Rawat, Seitomer, and Gokhale describes generating constraints from a stored user profile, applying metadata/images of an already selected prospective home to an Machine-Learning program, or outputting a GUI with an indicator. Applicant further argues Rawat only displays general attributes, Seitomer is unrelated to prospective homes, and Gokhale only filters lists rather than displaying updated information for an already-selected home.
Applicant’s arguments improperly attack the references individually rather than the combination as a whole. The test for obviousness is not whether one reference alone teaches all limitations, but whether the combined teachings of the prior art would have suggested the claimed invention to a person of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Regarding the argument that Gokhale does not apply to an already-selected home and the claims do not recite the limitation “for an already-selected home”. Rawat explicitly discloses a user selecting and viewing a specific, individual prospective home via a “real estate property view page” (Rawat, Fig. 2, col. 4 Ln. 10-24). Gokhale teaches generating affordability constraints based on a user’s financial profile (para. [0032]-[0033]).
The Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Regarding to the arguments on claims 4 and 14 on page 13, Applicant argues Seitomer merely discloses a spreadsheet and does not disclose a GUI including selectable list containing a numeric value of each additional home cost and an excerpt of the text-based metadata or image describing the output feature.
The argument is unpersuasive. Again, the Applicant attacks Seitomer in isolation. The rejection relies on the combination of the references. The Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The dependent claims arguments are depended to the independent claims and are found unpersuasive.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Morgan (US 20220198588 A1) is directed to computer-implemented systems and methods for comparing real estate properties. In one aspect a real estate mobile application is provisioned, wherein the application has a compare home engine. The application allows users to select listings from a database and then filter the listings based on image views, such as kitchen, bathroom, etc. The compare home engine on the real estate mobile application reads metadata or otherwise uses an image search engine or algorithm to identify and tag with metadata the images from the database. The application then displays the tagged images in an easy viewing format so that the user can more readily compare the various rooms.
O. Poursaeed, T. Matera, S. Belongie, “Vision-based Real Estate Price Estimation” submitted on 18 July 2017; Computer Vision and Pattern Recognition; https://doi.org/10.48550/arXiv.1707.05489; teaching a method for price estimation using dataset of real estate photos and metadata using computer vision learning algorithm.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WENREN CHEN/Primary Examiner, Art Unit 3626
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