DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 15, 2025 has been entered.
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 Non-Final Office Action in response to amended claims filed on November 18, 2025, 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 maintained in light of the amendment.
Previous 35 U.S.C. 103 rejection has been withdrawn and new 35 U.S.C. 103 rejection has been added in light of the amendment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:
The present application and patent claimed the same home cost analysis server and computer-implemented method for identifying home costs, 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 training/sample datasets of images and text-based metadata of homes into the machine learning program; receive 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 trained machine-learning program, which outputs at least one feature of the prospective home; access an external database storing historical insurance claim information including historical additional (i.e., ancillary) costs associated with homes in the target geographic area, wherein the additional costs are related to at least one feature of a respective home; perform a lookup in the external database to retrieve comparable historical additional (i.e., ancillary) costs from corresponding homes having a similar or comparable feature to the at least one output feature of the prospective home; output, to a user, a selectable list of any additional (i.e., ancillary) home cost associated with each at least one output feature; receive user input indicating a selection of at least one additional home cost; and in response to the user input, output, to the user, an anticipated home cost associated with the prospective home that includes the selected additional (i.e., ancillary) home cost.
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 to identify features of homes from text-based metadata and at least one image associated with those homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program;
receive, via a graphical user interface (GUI) displayed on a user computing device of a user, user input including a prospective home in a target geographic area;
in response to the user input of the prospective home, generate one or more constraints using a retrieved user profile for the user;
input text-based metadata and one or more images of the prospective home as inputs to the trained machine-learning program, which outputs at least one feature of the prospective home;
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;
using an identifier of the at least one output feature of the prospective home, perform a lookup in the external database 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 including an initial home cost and a selectable list of any additional home cost associated with each at least one output feature, 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;
in response to the user input, cause an update of the GUI 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.
The functional steps of two claims are identical.
The only difference between present application and ‘499 is the lexicographical difference, wherein “training dataset” is representative of “sample dataset” in ‘499; “trained machine-learning program” is representative of “machine-learned feature processing program” in ‘499; “historical insurance claim information including historical additional cost” is representative of “historical ancillary cost” in ‘499; However, the functional steps and result are the same for the same home cost analysis.
Patent ‘499 discloses a more specific and detail description of steps using user computer device with interface; ancillary cost; and generating of machine-learned feature processing program, which is encompass a specie, while present application is presented with broader claim language of the same functional steps, encompassing a genus.
It would have been obvious for the specie of ‘499 to include the genus as described in the present application.
The amended additional of “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” is similar to the what is already claimed in Pat. No. 11,151,669, reciting: “using at least one graphical indicator that relates the one or more ancillary home costs to an anticipated overall monthly home cost.”
Because the parent patents already claim the system that generates the same image data and metadata, comparing the data to user’s (i.e. homebuyer) budget/profile and changing the GUI color/text based on the comparison is common and obvious practice, the present claim does not represent a patently distinct invention.
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 to identify features of homes from text-based metadata and at least one image associated with those homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program;
receiving, via a graphical user interface (GUI) displayed on a user computing device of a user, user input including a prospective home in a target geographic area;
in response to the user input of the prospective home, generating one or more constraints using a retrieved user profile for the user;
inputting text-based metadata and one or more images of the prospective home as inputs to the trained machine-learning program, which outputs at least one feature of the prospective home;
accessing 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;
using an identifier of the at least one output feature of the prospective home, performing a lookup in the external database 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 including an initial home cost and 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 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-10 are directed to a system (i.e., a machine) and claims 11-20 are directed to a method (i.e., a process). Thus, the eligibility analysis proceeds to Step 2A.1.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1 and 11, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1 and 11, is
train a machine-learning program to identify features of homes based upon text-based metadata and at least one image associated with those homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program; (machine-learning program is interpreted to be a mathematical model)
receive user input including a prospective home in a target geographic area;
in response to the user input of the prospective home, generate one or more constraints using a retrieved user profile for the user;
input text-based metadata and one or more images of the prospective home as inputs to the trained machine-learning program, which outputs at least one feature of the prospective home;
access 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;
using an identifier of the at least one output feature of the prospective home, perform a lookup 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, an initial home cost and 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;
in response to the user input, cause an update 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.
The claimed invention is directed to an abstract idea of identifying home costs.
The limitations above suggest a process similar to collecting information (steps [B], [D], [E], and [H]), analyzing information (steps [A], [C], and [F]) and presenting the information (steps [G] and [I]). Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite a fundamental economic practice long prevalent in our system of commerce in the form of advertising, marketing, or sales activity or behaviors for home searching and marketing in real estate industry (as discussed in app. Specification [0003], “When a person, such as a prospective home buyer, is interested in purchasing a new home, the prospective home buyer may conduct their own searching for available homes, or may hire a real estate agent to assist in such a search.”). Under the broadest reasonable interpretation, other than the additional elements of computer components, the limitations recite a process of collecting information regarding features for home searching and identifying comparable additional home cost associated with prospective homes for buyers to review. Because the limitations above closely follow the steps standard in commercial interaction for a business practice of providing a home buying service, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Additionally and alternatively, under the broadest reasonable interpretation in view of the specification, the step [A] training a machine-learning program requires specific mathematical calculation (algorithm), which amounts to forms of performing mathematical calculations, the claims recite an abstract idea consistent with the “mathematical concepts” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(I).
“For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the claimed invention falls within the mental process/certain method of organizing human activity grouping of abstract ideas, and the step falls within the mathematical concepts grouping of abstract ideas. Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
The additional elements considered include:
Claim 1: “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:”; “machine-learning program”; “via a graphical user interface (GUI) displayed on a user computing device of a user”; “external database”, “in the external database”; “updated GUI”;
Claim 11: “using a home cost analysis server including one or more processors in communication with one or more memory devices,” “machine-learning program”; “via a graphical user interface (GUI) displayed on a user computing device of a user”; “external database”, “in the external database”; “updated GUI”;
The additional element of a system comprising generic computer elements are found to recite mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer to transmit, manipulate, and display information is not an improvement to a technology or technical field. That is, the additional elements merely recite computer elements to train, receive, input, access, lookup, and output information. The additional element is recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components, i.e., these generic computing elements are merely being used to perform the tasks of the abstract idea, see MPEP 2106.05(f). There is no indication from the specification that the computer elements are anything but generic hardware and/or software, and the combination of elements is simply a generic computing system (see Applicant’s Specification at least at paragraphs [0102]-[0107] describing generic computing devices and server; [0112]-[0116] describing generic implementation of machine-learning model). In conclusion, the function of limitations [A]-[I] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer.
Accordingly, alone and in combination, these additional element(s) do not integrate the abstract idea into a practical application. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations of [A]-[I] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claims as a whole merely describes how to generally “apply” the concept for identifying home costs. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2-3 and 12-13, the claims further recite additional descriptive information regarding to the historical insurance claim information. The additional descriptive information does not change the abstract idea of the independent claims. No additional element has been recited. The claims are ineligible.
As for dependent claims 4-5 and 14-15, the claims further recite additional descriptive information regarding to the selectable list can include. The additional descriptive information does not change the abstract idea of the independent claims. No additional element has been recited. The claims are ineligible.
As for dependent claims 6-8 and 16-18, the claims further recite additional descriptive information regarding to the expected time value and singular time value. The additional descriptive information does not change the abstract idea of the independent claims. No additional element has been recited. The claims are ineligible.
As for dependent claim 9-10 and 19-20, the claims further recite additional abstract steps of accessing, receiving, analyzing, and outputting information, which do not change the abstract idea of the independent claims. The step is recited at a high level of generality (i.e., as a generic computer system performing generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101.
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).
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 to identify features of homes from text-based metadata and at least one image associated with those homes, by inputting training datasets of images and text-based metadata of homes into the machine learning program (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”; and 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 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);
in response to the user input of the prospective home, generate one or more constraints (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);
input text-based metadata and one or more images of the prospective home as inputs to the trained machine-learning program, which outputs at least one feature 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 “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 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 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 a retrieved 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 the external database 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 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 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 the external database 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 (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 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 a retrieved 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 a retrieved user profile for the user (para. [0021] and [0031] teaches the system finds all properties which a user can afford based on user set financial parameters such as down payment and maximum monthly payment);
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 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.
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:
The Applicant’s remarks are fully considered, however, they are found to be unpersuasive.
Per remarks on pages 8-11, Applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Here, examiner identified the following steps as part of the abstract idea: training of a mathematical model and presenting of information to user. The computer processor, memory, program, and GUI, etc. are considered additional elements, which are merely facilitating the tasks of said abstract idea. MPEP 2106.05(f) is clear that this generic recitation does not integrate the abstract idea into practical application and/or add significantly more. This interpretation holds whether the additional elements are viewed alone or in combination, where the combination of elements is nothing more than a network-enabled computing system. (Examiner notes that the phrase "well-understood, routine, and conventional" was not used in the eligibility analysis. Instead, examiner relied on MPEP 2106.05(f), as explained above.)
On bottom of page 10 to page 11, the Applicants asserts the claim limitations involves more than performance of well understood, routine, [and] conventional activities previously known to the industry. The Applicant further asserts 101 rejection must be withdrawn under Berkheimer because it is unconventional to train machine-learned program to identify, based upon input images and text-based metadata, feature of a home; …
Upon reviewing the Specification [0075] and [0112]-[0115], the use of machine-learning programs with metadata for analysis is a result-based solution that the computer system of the invention is able to make such analysis and determination without high-level of detail to what each machine learning programs are, the differences, nor how the training process is performed to achieve the desired result. The Specification itself is admission to the limitations are well-known and can be accomplished without further required technological detail.
See 2106.05(d)(I)(2) - “an examiner should determine that an element (or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry. The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 ( Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph); see also Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463, 221 USPQ 481, 489 (Fed. Cir. 1984) ("[T]he specification need not disclose what is well known in the art."); In re Myers, 410 F.2d 420, 424, 161 USPQ 668, 671 (CCPA 1969) ("A specification is directed to those skilled in the art and need not teach or point out in detail that which is well-known in the art."); Exergen Corp., 725 Fed. App’x. 959, 965 (Fed. Cir. 2018)”
As result, the Office asserts, the additional elements in the claims are recited at a high-level of generality to perform the (generic computer functions of training, receiving, inputting, access, and outputting information) steps of the abstract idea, amounts to more than instructions to apply the abstract idea on a computer, as a tool which is discussed in MPEP 2106.05(f). When considered in combination, the additional elements do not integrate the abstract idea into a practical application or amounts to significantly more than the judicial exception (i.e., abstract idea).
Thus, the 101 rejection is maintained.
35 U.S.C. 103 Rejection:
The Examiner asserts that the applicant’s arguments are directed towards amended claim limitations and are, therefore, considered moot. However, the Examiner has responded to the amended amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments. New reference Gokhale has been introduced to teach the amended claims.
On page 13, the Applicant’s concern regarding to Office Action stating descriptive language of the claims. The Examiner would like to clarify, the Office Action provides citation to each of the claims and the Examiner’s note incorporating MPEP § 2111.04 and 2111.05 for descriptive information is additional information for clarity purposes. The entirety of the recitations of the claims have been considered. Thus, the rejection has been maintained.
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
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/WENREN CHEN/Primary Examiner, Art Unit 3626