DETAILED ACTION
The following is a Non-Final Office Action in response to communications filed February 11, 2026. Claims 1–2, 4–5, 14–15, 17–18, and 20 are amended; claims 13, 16, and 19 are canceled; and claims 21–22 are newly added. Currently, claims 1–2, 4–11, 14–15, 17–18, and 20–22 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 11, 2026 has been entered.
Response to Amendment/Argument
With respect to the rejection of claims under 35 U.S.C. 101, Applicant’s remarks are directed to newly amended subject matter, which is addressed for the first time herein.
Additionally, Examiner notes that Applicant’s remarks are not commensurate with the scope of the claims. Applicant repeatedly asserts that the recited machine learning model performs all of the limitations of claims 1 and 22. However, as currently presented, the machine learning model is limited to “identifying … one or more parts of the vehicle”. The machine learning model does not perform any of the remaining claim elements. Further, even if the machine learning model performed the remaining steps, Examiner maintains that the machine learning model would neither integrate the abstract idea into a practical application under Step 2A Prong Two nor amount to significantly more than the abstract idea under Step 2B because the machine learning model would do no more than generally link the recited business functions to a generic machine learning environment. The claims do not include any elements reciting the specifics of a machine learning or training process, and broadly using a machine learning model to, for example, identify damage would not embody any improvements in machine learning technology. As a result, Applicant’s remarks are not persuasive.
With respect to the rejection of claims under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are moot in view of the updated grounds of rejection asserted below.
Claim Objections
Claims 1 and 20 are objected to because of the following informalities:
Claims 1 and 20 recite “determining, based on the incurred damage of the external part and the first confidence value associated with the with the incurred damage to the external part”. Examiner recommends amending the element to recite “determining, based on the incurred damage of the external part and the first confidence value associated .
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1–2, 4–11, 14–15, 17–18, and 20–22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As amended, claims 1 and 20 recite “determining, based on the incurred damage of the external part and the first confidence value associated with the with the incurred damage to the external part, whether an internal part of the vehicle has incurred damage, wherein the incurred damage includes a second confidence value associated with the incurred damage to the internal part”. Although paragraphs 27–36 of Applicant’s Specification disclose determining confidence values for both internal and external damage, the Specification does not disclose determining internal damage based on an external confidence value or determining an internal confidence value based on an external confidence value. As a result, the amended subject matter is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention, and claims 1 and 20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claims 2, 4–11, 14–15, 17–18, and 20–21, which depend from claim 1, inherit the deficiencies described above. As a result, claims 2, 4–11, 14–15, 17–18, and 20–21 are similarly rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–2, 4–11, 14–15, 17–18, and 20–22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 20 recite “identifying … that an external part … incurred damage, wherein the incurred damage includes a first confidence value associated with the incurred damage to the external part” and “determining, based on the incurred damage of the external part … whether an internal part of the vehicle has incurred damage, wherein the incurred damage includes … the incurred damage to the internal part”. Examiner submits that the varying recitations of “incurred damage” render the scope of the claims indefinite because it is unclear how Applicant intends for each recitation to reference either the external part of the internal part.
For purposes of examination, and in view of the objection raise above, the claims are interpreted as reciting:
identifying, for at least the first image, [[that]] incurred damage to an external part of the one or more parts of the vehicle to the external part includes a first confidence value associated with the incurred damage to the external part;
determining, based on the incurred damage [[of]] to the external part and the first confidence value associatedto the internal part, wherein the incurred damage to the internal part includes a second confidence value associated with the incurred damage to the internal part
In view of the above, claims 1 and 20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2, 4–11, 14–15, 17–18, and 20–21, which depend from claim 1, inherit the deficiencies described above. As a result, claims 2, 4–11, 14–15, 17–18, and 20–21 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2 and 22 recite “the one part of the one or more parts” in lines 2–3 and 7–8, respectively. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, the claims are interpreted as reciting “[[the]] one part of the one or more parts” in lines 2–3 and 7–8, respectively.
Claims 17–18 recite “the confidence value of damage to the internal part” in lines 2 and 2, respectively. There is insufficient antecedent basis for this limitation in the claim.
For purposes of examination, claim 17 is interpreted as reciting “the second confidence value [[of]] associated with the incurred damage to the internal part”.
Claim 21 recites “the machine learning models” in lines 2 and 3. There is insufficient antecedent basis for these limitations in the claim.
For purposes of examination, claim 21 is interpreted as reciting “the machine learning model” in lines 2 and 3 and further reciting “wherein the machine learning model determines
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
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 1–2, 4–11, 14–15, 17–18, and 20–22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–2, 4–11, 14–15, 17–18, and 20–22 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements for “receiving a series of images of a vehicle from one or more viewpoints”; “identifying, for at least a first image from the series of images, one or more parts of the vehicle captured in the first image”; “identifying, for at least the first image, that an external part of the one or more parts of the vehicle incurred damage, wherein the incurred damage includes a first confidence value associated with the incurred damage to the external part”; “determining, based on the incurred damage of the external part and the first confidence value associated with the with the incurred damage to the external part, whether an internal part of the vehicle has incurred damage, wherein the incurred damage includes a second confidence value associated with the incurred damage to the internal part”; and “determining, based on the first confidence value and the second confidence value, whether the vehicle is a total loss or repairable.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for commercial sales activities or behaviors because the elements describe a process for evaluating vehicle damage. Further, with the exception of the element for “receiving”, the elements identified above recite mental processes because the elements describe observations or evaluations that can be practically performed in the mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 20 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 20 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2, 4–11, 14–15, 17–18, and 20–21 further describe the process for evaluating vehicle damage and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 2, 4–11, 14–15, 17–18, and 20–21 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a machine learning model and a classifier. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 20 includes substantially similar limitations to those included with respect to claim 1. Although claim 20 further includes a memory and processor, the additional elements, when considered in view of the claim as a whole, do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea. As a result, claim 20 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 5, 10, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an AI system (claims 5 and 10) and an element for “training” machine learning models (claim 21). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use. As a result, claims 5, 10, and 21 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 do not include any additional elements beyond those included with respect to the claims from which claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 depend. As a result, claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a machine learning model and a classifier. The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claim 20 includes substantially similar limitations to those included with respect to claim 1. Although claim 20 further includes a memory and processor, the additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 20 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 5, 10, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an AI system (claims 5 and 10) and an element for “training” machine learning models (claim 21). The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 5, 10, and 21 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 do not include any additional elements beyond those included with respect to the claims from which claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 depend. As a result, claims 2, 4, 6–9, 11, 14–15, 17–18, and 22 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–2, 4–11, 14–15, 17–18, and 20–22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102(a)(1)
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, 14, and 20–21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tian et al. (U.S. 2021/0142464).
Claims 1 and 20: Tian discloses a method, comprising:
receiving a series of images of a vehicle from one or more viewpoints (See paragraph 17, wherein images are received from a customer);
identifying, for at least a first image from the series of images using a machine learning model, one or more parts of the vehicle captured in the first image using a first classifier for identifying parts of the vehicle (See paragraph 27, in view of paragraph 4, wherein parts of the vehicle are identified “using a classifier that is trained to associate respective portions of in an image of a structure with respective external parts of the structure to detect several of external parts of a structure in a first image of the structure”; see also paragraph 18);
identifying, for at least the first image, that an external part of the one or more parts of the vehicle incurred damage, wherein the incurred damage includes a first confidence value associated with the incurred damage to the external part (See FIG. 3 and paragraph 21, wherein a likelihood of damage is computed for external parts);
determining, based on the incurred damage of the external part and the first confidence value associated with the with the incurred damage to the external part, whether an internal part of the vehicle has incurred damage, wherein the incurred damage includes a second confidence value associated with the incurred damage to the internal part (See paragraphs 45–48, in view of paragraphs 21 and 29, wherein probabilistic estimates for internal part damage are generated based on external damage estimates, including damage likelihood and severity); and
determining, based on the first confidence value and the second confidence value, whether the vehicle is a total loss or repairable (See paragraphs 22 and 44, in view of paragraph 29, wherein an aggregate analysis of all parts of the vehicle is used to determine when the vehicle is a total loss or can be repaired).
With respect to claim 20, Tian further discloses a memory and processor (See claim 15).
Claim 14: Tian discloses the method of claim 1, further comprising: estimating a cost of repair for the internal part (See paragraph 43, wherein a cost of repair or replacement is generated for each internal part).
Claim 21: Tian discloses the method of claim 1, further comprising: training the machine learning models with images of vehicles having a type of exterior damage corresponding to internal damage, wherein the machine learning models determine whether the vehicle is a total loss or repairable based on the training (See paragraphs 24 and 28, in view of paragraph 41, wherein probabilistic networks for determining damage are built during a training phase).
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 2, 15, and 17–18 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (U.S. 2021/0142464) in view of Gronsbell et al. (U.S. 2020/0234515).
Claim 2: As disclosed above, Tian discloses the elements of claim 1.
Tian further discloses the method of claim 1, further comprising: when it is determined that the vehicle is repairable, determining the one part of the one or more parts of the vehicle that are to be replaced (See paragraph 21, wherein the system determines whether each part may be repaired or replaced); and
identifying, using the machine learning model, additional features of the one part of the one or more parts of the vehicle (See paragraphs 28, 34, and 40, wherein damage type, number and size, size of damage, and severity of damage are identified for each part). Tian does not expressly disclose the remaining claim elements.
Gronsbell discloses matching the one part of the one or more parts and the additional features to a list of available parts provided by a vendor (See paragraph 208, in view of paragraph 146, wherein replacement parts are matched to a diagnosis, and wherein parts are ordered according to both manufacturer replacement parts and third-party replacements);
determining a replacement part in the list of available parts that corresponds to the one part of the one or more parts (See paragraph 208, in view of paragraph 146, wherein replacement parts are matched to a diagnosis, and wherein parts are ordered according to both manufacturer replacement parts and third-party replacements); and
ordering the replacement part from the vendor (See paragraph 208, in view of paragraph 146, wherein replacement parts are ordered in the context of user preference data associated with manufacturer replacement parts and third-party replacements).
Tian discloses a system directed to assessing damage and repair costs in vehicles. Gronsbell discloses a system directed to monitoring and analyzing vehicle conditions. Each reference discloses a system directed to assessing vehicle conditions. The technique of matching and ordering parts is applicable to the system of Tian as they both share characteristics and capabilities, namely, they are directed to assessing vehicle conditions.
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Gronsbell to the teachings of Tian would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate vehicle condition assessments into similar systems. Further, applying a process to match and order parts to Tian would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 15: Although Tian discloses estimating a cost of repair for the internal part (See citations above), Tian does not expressly disclose the remaining elements.
Gronsbell discloses wherein the cost of repair is a range of values (See FIG. 18).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 17: Tian discloses the method of claim 1, further comprising: when the confidence value of damage to the internal part is determined, sending an alert indicating damage to the internal part, wherein the alert is sent to one of an insurer of the vehicle, a repair shop selected to repair the vehicle or an owner of the vehicle (See paragraphs 19–20, in view of paragraphs 29 and 44, wherein the damage report is presented to the adjuster). Tian does not expressly disclose the remaining claim elements.
Gronsbell discloses when an element satisfies a threshold, sending an alert (See FIG. 16 and 247, wherein information is displayed according to satisfaction of a distance threshold).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 18: Although Tian discloses the confidence value of damage to the internal part (See citations above), Tian does not expressly disclose the remaining claim elements.
Gronsbell discloses selecting a repair shop to repair the vehicle based on the damage (See FIG. 16 and paragraphs 204–208, wherein damage considerations are used to select a repair shop; see also paragraph 247).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claims 4–7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (U.S. 2021/0142464) in view of Gronsbell et al. (U.S. 2020/0234515), and in further view of Thurber et al. (U.S. 2023/0177616).
Claim 22: As disclosed above, Tian discloses the elements of claim 1. Tian does not disclose the elements of claim 22.
Gronsbell discloses receiving repair shop information comprising an availability and capabilities of each of a plurality of repair shops (See paragraphs 204 and 206–207, wherein repair shop availability and certifications are received);
selecting one of the plurality of repair shops (See FIG. 16 and paragraph 208, wherein the user schedules an appointment to address a remedial measure); and
scheduling repairs for the vehicle at the selected one of the plurality of repair shops based on a replacement part for the one part of the one or more parts at the selected one of the plurality of repair shops (See FIG. 16 and paragraph 208, wherein the user schedules an appointment to address a remedial measure, and wherein scheduling may include identifying and ordering necessary parts).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Tian and Gronsbell do not expressly disclose the remaining claim elements.
Thurber discloses an estimated delivery time for a replacement part (See paragraph 88, wherein an estimated time for repair is generated based on whether or not the parts need to be ordered; see also paragraphs 49–50).
As disclosed above, Tian discloses a system directed to assessing damage and repair costs in vehicles, and Gronsbell discloses a system directed to monitoring and analyzing vehicle conditions. Thurber discloses a system directed to evaluating property damage. Each reference discloses a system directed to assessing property conditions. The technique of utilizing delivery estimates is applicable to the systems of Tian and Gronsbell as they each share characteristics and capabilities, namely, they are directed to assessing property conditions.
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Gronsbell to the teachings of Tian and Gronsbell would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate property condition assessments into similar systems. Further, applying delivery estimates to Tian and Gronsbell would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 4: Tian does not expressly disclose the elements of claim 4.
Gronsbell discloses wherein selecting the one of the plurality of repair shops comprises: displaying the plurality of repair shops and repair shop information of each repair shop (See FIG. 16 and paragraph 247); and
receiving a user selection of one of the plurality of repair shops to repair or replace the one part of the one or more parts (See FIG. 16 and paragraph 208, wherein the user schedules an appointment to address a remedial measure; see also paragraph 247).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 5: Tian does not expressly disclose the elements of claim 5.
Gronsbell discloses wherein selecting the one of the plurality of repair shops is performed by an artificial intelligence (AI) system based on at least the one part of the one or more parts and the repair shop information for each of the plurality of repair shops (See paragraph 247, in view of paragraph 145, wherein repair scheduling is performed automatically, and wherein remedial measures are generated using an AI model; see also paragraph 208).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 6: Tian does not expressly disclose the elements of claim 5.
Gronsbell discloses wherein the repair shop information further comprises an amount of time to repair or replace the one part of the one or more parts, wherein the selected one of the plurality of repair shops is further based on an amount of time to repair or replace the one part of the one or more parts (See paragraph 247, wherein scheduling is optimized with respect to speed requirements; see also paragraphs 207 and 210, wherein time estimates are received from service providers).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 7: Tian does not expressly disclose the elements of claim 5.
Gronsbell discloses wherein the repair shop information further comprises a quality metric for each repair shop and the selected one of the repair shops is based on the quality metric relative to other ones of the plurality of repair shops, wherein the selected one of the plurality of repair shops is further based on the quality metric (See paragraph 247, wherein repair shop ratings are used to select providers; see also paragraphs 147 and 206).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claims 8–11 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (U.S. 2021/0142464) in view of Gronsbell et al. (U.S. 2020/0234515), and in further view of Thurber et al. (U.S. 2023/0177616) and Horn et al. (U.S. 2017/0220998).
Claim 8: As disclosed above, Tian, Gronsbell, and Thurber disclose the elements of claims 1, 22, and 4. Tian does not expressly disclose the elements of claim 8.
Gronsbell discloses receiving company information for each of a plurality of companies (See paragraphs 204 and 206–207, wherein repair shop availability and certifications are received); and
selecting one of the plurality of companies (See FIG. 16 and paragraph 208, wherein the user schedules an appointment to address a remedial measure).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Tian, Gronsbell, and Thurber do not expressly disclose the remaining claim elements.
Horn discloses receiving towing company information for each of a plurality of towing companies; and selecting one of the plurality of towing companies to tow the vehicle to the selected one of the plurality of repair shops (See FIG. 9 and paragraphs 87–88, wherein a towing service provider is selected based on provider information that is received and stored in a provider data store).
As disclosed above, Tian discloses a system directed to assessing damage and repair costs in vehicles, Gronsbell discloses a system directed to monitoring and analyzing vehicle conditions, and Thurber discloses a system directed to evaluating property damage. Horn discloses a system directed to managing service requests, including automobile accident services. Each reference discloses a system directed to managing vehicle conditions. The technique of utilizing towing providers is applicable to the systems of Tian, Gronsbell, and Thurber as they each share characteristics and capabilities, namely, they are directed to managing vehicle conditions.
One of ordinary skill in the art would have recognized that applying the known technique of Horn would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Horn to the teachings of Tian, Gronsbell, and Thurber would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate vehicle condition management into similar systems. Further, applying towing providers to Tian, Gronsbell, and Thurber would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 9: Tian does not expressly disclose the elements of claim 9.
Gronsbell discloses wherein selecting the one of the plurality of companies comprises: displaying the plurality of companies and company information (See FIG. 16 and paragraph 247); and
receiving a user selection of the one of the plurality of companies (See FIG. 16 and paragraph 208, wherein the user schedules an appointment to address a remedial measure; see also paragraph 247).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Tian, Gronsbell, and Thurber do not expressly disclose the remaining claim elements.
Horn discloses selecting the one of the plurality of towing companies (See FIG. 9 and paragraphs 87–88, wherein a towing service provider is selected).
One of ordinary skill in the art would have recognized that applying the known technique of Horn would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Claim 10: Tian does not expressly disclose the elements of claim 10.
Gronsbell discloses wherein selecting the one of the plurality of companies is performed by an artificial intelligence (AI) system based on at least the company information (See paragraph 247, in view of paragraph 145, wherein repair scheduling is performed automatically, and wherein remedial measures are generated using an AI model; see also paragraph 208).
One of ordinary skill in the art would have recognized that applying the known technique of Gronsbell would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2. Tian, Gronsbell, and Thurber do not expressly disclose the remaining claim elements.
Horn discloses selecting the one of the plurality of towing companies (See FIG. 9 and paragraphs 87–88, wherein a towing service provider is selected).
One of ordinary skill in the art would have recognized that applying the known technique of Horn would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Claim 11: Tian, Gronsbell, and Thurber do not expressly disclose the elements of dependent claim 11.
Horn discloses wherein the towing company information comprises one of a distance of a tow, a distance the vehicle is from a towing company, a flat fee for the tow, or a flat fee plus distance charge for the tow (See paragraph 88, wherein providers are selected according to distance information).
One of ordinary skill in the art would have recognized that applying the known technique of Horn would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Faga et al. (U.S. 2022/0036463) discloses a system directed to estimating vehicle damage using image analysis.
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/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623