Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-21 have been examined.
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.
Independent Claims 1, 15, 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are in a statutory category of invention. However, the claims recite receive, a request to repair a vehicle, the vehicle including a damaged portion; prompt the user by to transmit one or more images of the damaged portion of the vehicle; inputting the one or more images of the damaged portion of the vehicle, determine damage to the vehicle based on the one or more images of the vehicle and determine one or more repair facilities capable of performing repairs to the damage to the vehicle; and cause the one or more repair facilities to be displayed. This is considered in the Abstract Idea grouping of certain methods of organizing human activity - advertising, marketing or sales activities or behaviors. This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea with additional generic computer elements. The additional elements are considered from the user computing device, displaying a prompt on a user interface of the user computing device, execute the machine learning model. These are considered generic. The machine learning is considered generic and just “apply it”. This claim is considered routine car accident insurance claim processing with an “apply it” of input images and a generic machine learning. No functional or technical steps for how the image analysis or machine learning actually functions are in the claims. The generically recited computer elements do not add a practical application or meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations only perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). Also, the additional hardware elements are: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions. Viewed separately or as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amounts to significantly more than the abstract idea itself. The claim does not provide significantly more than the identified abstract idea, in that there is no improvement to another technology or technical field, no improvement to the functioning of a computer, no application with, or by use of a particular machine, no transformation or reduction of a particular article to a different state or thing, no specific limitation other than what is well-understood, routing and conventional in the field, no unconventional step that confines the claim to a particular useful application, or meaningful limitations that amount to more than generally linking the use of the abstract idea to a particular technological environment. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Dependent claims 2-14, 16-20 are not considered directed to any additional non-abstract claim elements. No technical or functional details for the image analysis or machine learning are found in the dependent claims. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above. While these descriptive elements may provide further helpful description for the claimed invention, these elements do not confer subject matter eligibility to the invention since their individual and combined significance is still not more than the abstract concepts identified in the claimed invention. Hence, these dependent claims are also rejected under 101.
Please see the 35 USC 101 section at the Examination Guidance and Training Materials page on the USPTO website.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6, 7, 10-16, 18-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Franke (20170148102).
Claims 1, 15, 21. Franke discloses a computer system for identifying repairs for a vehicle using a machine learning model, the computer system comprising at least one processor in communication with at least one memory device, the computer system in communication with a user computing device associated with the user, the at least one processor is programmed to (see machine learning at [56, 138, 174]):
receive, from the user computing device, a request to repair a vehicle, the vehicle including a damaged portion (see damage and vehicle and image at [41] and repair at [51]);
prompt the user by displaying a prompt on a user interface of the user computing device to transmit one or more images of the damaged portion of the vehicle (see damage and vehicle and image at [41] and repair at [51]);
execute the machine learning model by inputting the one or more images of the damaged portion of the vehicle, the machine learning model configured to determine damage to the vehicle based on the one or more images of the vehicle and determine one or more repair facilities capable of performing repairs to the damage to the vehicle (see analyze and damage and suggest and repair at [148]; see machine learning at [56, 138, 174]); and
cause the one or more repair facilities to be displayed on the user computing device (see suggest and repair at [148]).
Claim 2, 16. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to: generate, by the at least one processor, a plurality of questions about current conditions of the vehicle; transmit, from the at least one processor to the user computer device, a plurality of instructions to cause the user interface of the user computer device to display the plurality of questions about the current conditions of the vehicle; and receive, by the at least one processor from the user computer device via the user interface of the user computer device, a plurality of answers about the current conditions of the vehicle in response to the plurality of questions (see interactive and comments and information requests at [62]).
Claim 3. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to receive an indication from the user of whether the user plans to repair the vehicle (see approved by owner at [9]).
Claim 4, 18. Franke further discloses the computer system of claim 3, wherein the at least one processor is further programmed to: request, by the at least one processor from the user computer device via the user interface, one or more additional images of the vehicle based on an indication of whether the user plans to repair the vehicle, wherein the request includes instructions for taking the one or more images of the vehicle; and receive, by the at least one processor from the user computer device via the user interface, the one or more additional images of the vehicle captured by a camera of the user computer device (see interface which lets owner upload images at any time at [51, 182]; see first image with identifier at [126] then subsequent image for repair at [133]).
In further regards to claim 18, Franke further discloses the one or more images of the vehicle based on the plurality of answers (further see objective information and also interactive and comments and information requests at [62]).
Claim 6, 19. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to: retrieve, by the at least one processor, a calendar of appointments from an inspection facility computer device associated with a selected inspection facility; receive, by the at least one processor via the user interface, a user selection of a date and time for an appointment; determine a conflict between the user selection of the date and time for the appointment and the calendar of appointments of the selected inspection facility; and transmit an alternative date and time for an appointment to the user computing device based on the calendar of appointments (“[75]… This step may also usefully be integrated into a network-based workflow to provide the owner with automated or manual scheduling into available time slots for the repair facility.” And “[89]… This may be performed automatically, e.g., where an owner requests available time slots or advertises availability, or through a manual scheduling process negotiated between the owner and the adjuster.”).
Claim 7, 20. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to: receive, from the user computer device via the user interface, a search request including a name of a repair facility; query one or more databases to recognize the repair facility based on the name; if the name is recognized, retrieve, from one or more databases, facility information about the repair facility; determine, via the one or more databases, whether the repair facility is a select service location based on the facility information; and request that the user reenter the name of the repair facility if the name is not recognized (”[9]… a repair facility approved by an insurer.”, “[20]… a plurality of repair professionals over a data network and requesting responsive bids from the repair professionals.”, “[88]… the server 122 may select a suitable repair facility based on, e.g., price, availability, owner preference”).
Claim 10. Franke further discloses the computer system of claim 1, where in the at least one processor is further programmed to receive, from the user computing device, information about the vehicle, wherein the information about the vehicle includes one or more of a make of the vehicle, a model of the vehicle, a year of the vehicle, a location of damage on the vehicle, identification of the vehicle, identification of the user, or the one or more images of the vehicle ([56]).
Claim 11. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to train the machine learning model to determine damage to the vehicle and to determine one or more repair facilities capable of performing repairs on the vehicle based on a first subset of vehicle damage data (see train at [174, 175], see machine learning at [56, 138, 174]).
Claim 12. Franke further discloses the computer system of claim 11, wherein the at least one processor is further programmed to train the machine learning model to determine damage to the vehicle and to determine one or more repair facilities capable of performing repairs on the vehicle based on a second subset of vehicle repair data (see train at [174, 175], see machine learning at [56, 138, 174]).
Claim 13. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to: execute the machine learning model to analyze a plurality of answers from a plurality of vehicles and a plurality of selected repair facilities from a plurality of users; and determine one or more repair facilities to qualify as select service locations based on the executed machine learning model (see responsive bids which reads on answers at [20]; see range of acceptable bids at [57, 58]; “[88]… That is, once the owner approves a repair, the server 122 may select a suitable repair facility based on, e.g., price, availability, owner preference, or any number of automatically or manually applied criteria.”).
Claim 14. Franke further discloses the computer system of claim 13, wherein the at least one processor is further programmed to determine one or more attributes that make a user more or less likely to choose a repair facility based on the executed machine learning model (see owner preference at [88, 137, 140]; see preferred at [130]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5, 9, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Franke (20170148102).
Claim 5, 17. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to transmit, from the at least one processor to the user computer device, an electronic message including instructions to be displayed on the user computer device, wherein, additional instructions to be transmitted to the user computer device to cause display of the user interface on the user computer device (“[9]… The processor may be configured to provide updates to the owner on the damage assessment and repair process through at least one of electronic mail, instant message, telephone voice message, and updating a webpage accessible by the owner.”).
Franke does not explicitly disclose display a link to a user interface or when the link is selected, the link causes. That is, Franke does not explicitly disclose that the message has a link. However, Franke discloses a webpage accessible by the owner [9] and the message to owner above [9] and using hyperlinks for further info [89] and links for further info [121]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Franke’s hyperlink/link to Franke’s messages to owner and Franke’s website accessible by owner. One would have been motivated to do this in order to better make the website accessible by owner.
Claim 9. Franke further discloses the computer system of claim 1, wherein in response to the request to repair the vehicle, the at least one processor is further programmed to generate a user interface, wherein content is transmitted to the user computer device in at least one of a short message service (SMS) message, a multimedia messaging server (MMS) message, and an email message (“[9]… The processor may be configured to provide updates to the owner on the damage assessment and repair process through at least one of electronic mail, instant message, telephone voice message, and updating a webpage accessible by the owner.”).
Franke does not explicitly disclose generate a link or wherein the link is transmitted. That is, Franke does not explicitly disclose that the message has a link. However, Franke discloses a webpage accessible by the owner [9] and the message to owner above [9] and using hyperlinks for further info [89] and links for further info [121]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Franke’s hyperlink/link to Franke’s messages to owner and Franke’s website accessible by owner. One would have been motivated to do this in order to better make the website accessible by owner.
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Franke (20170148102) in view of Graessley (20090157289).
Claim 8. Franke further discloses the computer system of claim 1, wherein the at least one processor is further programmed to: receive, from the user computer device via the user interface, a search location for a repair facility, wherein the search location includes at least one of an address, a zip code, a municipality, or a present location of the user computer device (“[148]… a report may identify the damage, estimate an impairment to vehicle value, estimate repair costs, indicate parts and labor required for a repair, suggest suitably qualified and approved repair shops within a geographic area, or otherwise summarize the nature of the damage and the costs and steps required for repair”); wherein the machine learning model is further configured to determine a threshold distance based on a condition of the vehicle; query, one or more databases, to determine a plurality of repair facilities within the threshold distance of the search location; and present, to the user computer device via the user interface, the plurality of repair facilities (“[148]… a report may identify the damage, estimate an impairment to vehicle value, estimate repair costs, indicate parts and labor required for a repair, suggest suitably qualified and approved repair shops within a geographic area, or otherwise summarize the nature of the damage and the costs and steps required for repair”).
Franke does not explicitly disclose a safe distance based on the condition of the vehicle. However, Graessley discloses failure of the vehicle and calculating fuel and repair locations in distance/range and also other criteria or filters for repair stations [69] and also improving road safety of the driver [15]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Graessley’s repair shops in range based on condition and status of the vehicle to Franke’s vehicle needing repair and geographically proximate repair locations. One would have been motivated to do this in order to better repair vehicles and in a safe manner (as Graessley discloses with safety of the driver at [15]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Note Allowed parents CONS;
b) Brandmaier discloses machine learning for car accident image analysis and repair suggestion;
c) Burgess discloses repair and distance from repair criteria Burgess [0038].
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/ARTHUR DURAN/Primary Examiner, Art Unit 3622 4/22/26
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