DETAILED ACTION
The following FINAL Office Action is in response to Applicant’s Response filed on 06/29/2026.
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 . 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.
Status of Claims
Claims 1-20 were previously pending and subject to a non-final Office Action mailed 01/28/2026. Claims 1, 6, 12-13, 17, and 19 were amended. Claims 1-20 are currently pending and are subject to the final Office Action below.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 1-11, filed 06/29/2026, with respect to the 35 U.S.C. 101 rejections of Claims 1-2, 4-12, and 14-20 have been fully considered and are not persuasive.
Regarding Applicant’s arguments on page 1-3, Examiner respectfully disagrees. Providing an improvement on static or historical pricing models and subjective manual condition assessments is not an improvement in technology, but an improvement in the abstract idea itself as the predicted price is more accurate. See MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Regarding Applicant’s arguments on page 3-7, Examiner respectfully disagrees as the limitations which Applicant relies upon are part of the abstract idea. Additionally, the “technological architecture” or additional elements amount to mere instructions to apply an exception using a generic computer, extra-solution activity, and/or field of use.
Applicant’s arguments would be more persuasive if there were more “visual recognition” features recited within the claim as currently, the visual recognition engine amounts to mere field of use.
Regarding the Step 1 arguments, Applicant added the term “non-transitory” to Claim 17 and thus, Examiner removed the “signal per se” rejection as it was rendered moot.
Regarding the prong 1 arguments, Examiner respectfully disagrees as the receiving an item, generating a damage score, identifying dynamic price analysis data, and generating a predicted price limitations are directed to an abstract idea.
Regarding the prong 2 arguments, Examiner respectfully disagrees as the visual recognition engine and machine learning price prediction engine amount to field of use. See rejection below. The damage score and dynamic price analysis data are part of the abstract idea.
Regarding the argument that the claims improve item listing system functionality, Examiner respectfully disagrees. A condition-aware, market adaptive pricing architecture improves the price prediction which is an improvement in the abstract idea of commercial interactions. See MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Regarding the step 2B arguments, Examiner respectfully disagrees. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer, insignificant extra solution activity, and/or field of use which cannot provide an inventive concept.
Accordingly, the 35 U.S.C. 101 rejection is maintained.
35 USC § 103
Applicant’s arguments see pages 11-23, filed 06/29/2026, with respect to the 35 U.S.C. 103 rejections of Claims 1-20 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner relies on new references Southin and Nix to teach the independent claims.
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 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2 and 4-11 are directed to a system and Claims 17-20 are directed to non-transitory computer-storage media (i.e., a machine). Claims 12-16 are directed to a method (i.e., a process). Therefore, the claims all fall within one of the four statutory categories of invention.
Regarding Claims 1-2 and 4-11
Step 2A Prong 1
Independent Claim 1 recites the limitations of:
receiving an item image for an item…;
generating a damage score based on the damaged areas associated with item features of the item wherein the damage score represents a quantitative measure of a condition of the item;
identifying dynamic price analysis data associated with the item, the dynamic price analysis data comprising realtime inputs from continuously updated market data that enable dynamic price predictions;
generating a predicted price for the item… wherein … determines the predicted price based on the damage score and the dynamic price analysis data; and
communicating the predicted price to cause display of the predicted price.
The limitations of Claim 1 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“managing personal behavior or relationships or interactions between people” or “commercial or legal interactions”). Specifically, commercial interactions directed towards determining an item price for a seller. Therefore, the claims recite an abstract idea.
Paragraph 24-25 of the specification states “Mark is an independent sneaker reseller…He recently acquired a batch of Air Jordans, some of which have minor scuff marks and discoloration. Since the shoes vary in condition and age, pricing them correctly is a challenge…Without an item listing system that can quantify damage, factor in market trends, and optimize pricing dynamically, Mark struggles with inventory stagnation and lose revenue opportunities” which is further evidence of commercial interactions.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. Independent claim 1 recites the additional elements of: one or more computer processors, computer memory, a machine learning price prediction engine, and the limitation of “the item image”.
The additional elements of one or more computer processors, computer memory, and a machine learning price prediction engine are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
Additionally, the machine learning price prediction engine is also merely indicating a field of use or technological environment in which to apply a judicial exception. For example, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. See MPEP 2106.05(h). Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
The claim also recites “the item image captured using a visual recognition engine that isolates damaged areas of the item” limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea (“generating a damage score”) to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
Thus, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components and field of use which does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of one or more computer processors, computer memory, and a machine learning price prediction engine to perform the functions/steps recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Again, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. Such limitations amount to generally linking the judicial exception to a field of use or technological environment.
The claim also recites “the item image captured using a visual recognition engine that isolates damaged areas of the item” which limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea (“generating a damage score”) to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
None of the functions/steps of Claim 1 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, thus, the analysis does not change when considered as an ordered combination. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer and/or field of use which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1 is ineligible.
Dependent Claim 2 adds an additional element of an AR or MR visual recognition engine which “enables” functions such as real-time segmentation of visual data, calculating dimensions, and quantifying an impact. The limitation merely states that the item image is captured with AR or MR but does not describe how segmentation, calculating, and quantifying are achieved by the AR/MR visual recognition engine. This type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f).
Further, the limitation is merely indicating a field of use or technological environment in which to apply a judicial exception – specifically, limiting the image capture function to execution by the AR or MR visual recognition engine. Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
Dependent Claims 4-8 and 11 merely add additional limitations that narrow down the abstract idea identified above.
Dependent Claim 9 adds an additional element of an adaptive pricing and inventory engine that is integrated into an item listing workflow of an item listing platform.
Similarly, Dependent Claim 10 adds an additional element of a seller user interface interaction model that provides a plurality of item listing workflows based on user interface selections identified via a seller user interface and the predicted price.
Both limitations are merely indicating a field of use or technological environment in which to apply a judicial exception – specifically, limiting abstract idea by specifying the element through which a seller interacts with in order to list their item. Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
Additionally, the additional element of the adaptive pricing and inventory engine, item listing platform, seller user interface interaction model, and seller user interface are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components.
The dependent claims further narrow the identified abstract idea but do not otherwise alter the analysis presented above. Nothing in dependent claims 2, 4-11 when viewed alone or as an ordered combination, adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-2 and 4-11 are ineligible.
Regarding Claims 12-16
Step 2A Prong 1
Independent Claim 12 recites the limitations of:
accessing a damage score based on damage areas associated with item features of an item, wherein the item features of the item are identified from an item image…;
identifying dynamic price analysis data associated with the item, the dynamic price analysis data comprising real-time inputs from continuously updated market data that enable dynamic pricing predictions;
generating a predicted price for the item … wherein… determines the predicted price based on the damage score and the dynamic price analysis data, … enables price optimization by incorporating the damage score as a quantitative measure of a condition of the item
The limitations of Claim 12 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“managing personal behavior or relationships or interactions between people” or “commercial or legal interactions”). Specifically, commercial interactions directed towards determining an item price for a seller. Therefore, the claims recite an abstract idea.
Paragraph 24-25 of the specification states “Mark is an independent sneaker reseller…He recently acquired a batch of Air Jordans, some of which have minor scuff marks and discoloration. Since the shoes vary in condition and age, pricing them correctly is a challenge…Without an item listing system that can quantify damage, factor in market trends, and optimize pricing dynamically, Mark struggles with inventory stagnation and lose revenue opportunities” which is further evidence of commercial interactions.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. Independent claim 12 recites the additional elements of: a machine learning price prediction engine and the limitation of “updating” and “an item image”.
The additional element of a machine learning price prediction engine is recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
Additionally, the machine learning price prediction engine is also merely indicating a field of use or technological environment in which to apply a judicial exception. For example, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. See MPEP 2106.05(h). Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
The claim also recites “an item image captured using a visual recognition engine that isolates damaged areas of the item” which limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea (“accessing a damage score”) to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
“Updating one or more interface elements associated with an adaptive pricing and inventory management interface” is insignificant extra-solution activity such as post-solution data outputting or selecting a type of data to be manipulated. See MPEP 2106.05(g).
Thus, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components, field of use, and extra-solution activity which does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a machine learning price prediction engine to perform the functions/steps recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Again, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. Such limitations amount to generally linking the judicial exception to a field of use or technological environment.
The claim also recites “an item image captured using a visual recognition engine that isolates damaged areas of the item” which limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea (“accessing a damage score”) to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
“Updating one or more interface elements associated with an adaptive pricing and inventory management interface” is insignificant extra-solution activity such as post-solution data outputting or selecting a type of data to be manipulated. Applicant’s specification paragraph 116 states “Updating Interface Elements for Adaptive Pricing - A real-time mechanism that adjusts pricing elements dynamically, ensuring sellers always have access to the most accurate and optimized prices” and para. 131 states “At block 408, update one or more interface elements associated with an adaptive pricing and inventory management interface.” Thus, the interface elements and the interface are described in a way that shows that they are widely prevalent and/or in common use. See MPEP 2106.05(d)(II).
None of the functions/steps of Claim 12 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, thus, the analysis does not change when considered as an ordered combination. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer, insignificant extra solution activity, and/or field of use which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 12 is ineligible.
Regarding dependent Claim 13, the machine learning algorithms are also merely indicating a field of use or technological environment in which to apply a judicial exception. For example, specifying that the abstract idea of “wherein the damage score is generated” is performed by machine learning algorithms that generate standardized damage metrics for consistent assessments merely limits the claims to execution by the machine learning algorithms. See MPEP 2106.05(h). Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
Dependent Claims 14-16 merely add additional limitations that narrow down the abstract idea identified above.
The dependent claims further narrow the identified abstract idea but do not otherwise alter the analysis presented above. Nothing in dependent claims 13-16 when viewed alone or as an ordered combination, adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 12-16 are ineligible.
Regarding Claims 17-20
Step 2A Prong 1
Independent Claim 17 recites the limitations of:
communicating an item image for an item;
based on communicating the item image, receiving a predicted price, wherein the predicted price is generated for the item …, a damage score representing a quantitative measure of a condition of the item and dynamic pricing analysis data comprising real-time inputs from continuously updated market data that enable dynamic pricing predictions; and
causing display of the predicted price.
The limitations of Claim 17 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“managing personal behavior or relationships or interactions between people” or “commercial or legal interactions”). Specifically, commercial interactions directed towards determining an item price for a seller. Therefore, the claims recite an abstract idea.
Paragraph 24-25 of the specification states “Mark is an independent sneaker reseller…He recently acquired a batch of Air Jordans, some of which have minor scuff marks and discoloration. Since the shoes vary in condition and age, pricing them correctly is a challenge…Without an item listing system that can quantify damage, factor in market trends, and optimize pricing dynamically, Mark struggles with inventory stagnation and lose revenue opportunities” which is further evidence of commercial interactions.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. Independent claim 17 recites the additional elements of: one or more non-transitory computer-storage media, a computing system having a processor and memory, a machine learning price prediction engine, and the limitation of “the item image”.
The additional elements of one or non-transitory more computer-storage media, a computing system having a processor and memory, and a machine learning price prediction engine are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
Additionally, the machine learning price prediction engine is also merely indicating a field of use or technological environment in which to apply a judicial exception. For example, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. See MPEP 2106.05(h). Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
The claim also recites “the item image captured using a visual recognition engine that isolates damaged areas of the item” which limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
Thus, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components and field of use which does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of one or more non-transitory computer-storage media, a computing system having a processor and memory, and a machine learning price prediction engine to perform the functions/steps recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Again, specifying that the abstract idea of determining a predicted price is performed by a machine learning price prediction engine merely limits the claims to execution by the machine learning price prediction engine. Such limitations amount to generally linking the judicial exception to a field of use or technological environment.
The claim also recites “the item image captured using a visual recognition engine that isolates damaged areas of the item” which limits the abstract idea to specifically “damaged areas of the item” of the item image because limiting application of the abstract idea to a particular type of data is simply an attempt to limit the use of the abstract idea to a particular technological environment.
None of the functions/steps of Claim 17 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, thus, the analysis does not change when considered as an ordered combination. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer and/or field of use which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 17 is ineligible.
Dependent Claim 18 adds an additional element of an AR or MR visual recognition engine which “enables” functions such as real-time segmentation of visual data, calculating dimensions, and quantifying an impact. The limitation merely states that the item image is captured with AR or MR but does not describe how segmentation, calculating, and quantifying are achieved by the AR/MR visual recognition engine. This type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f).
Further, the limitation is merely indicating a field of use or technological environment in which to apply a judicial exception – specifically, limiting the image capture function to execution by the AR or MR visual recognition engine. Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
Dependent Claim 19 adds additional limitations that narrow down the abstract idea identified above (“communicating”). Dependent Claim 19 also adds an additional element of an item damage assessment interface and item damage assessment interface elements of an item listing system.
These limitations are merely indicating a field of use or technological environment in which to apply a judicial exception – specifically, limiting abstract idea by specifying the element through which a seller interacts with in order to communicate the seller’s input regarding item damage to the item listing system. Such limitations amount to generally linking the judicial exception to a field of use or technological environment which does not meaningfully limit the claim.
Additionally, the additional element of an item damage assessment interface and item damage assessment interface elements of an item listing system are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components.
Dependent Claim 20 merely adds additional limitations that narrow down the abstract idea identified above.
The dependent claims further narrow the identified abstract idea but do not otherwise alter the analysis presented above. Nothing in dependent claims 18-20 when viewed alone or as an ordered combination, adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 17-20 are ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-7, 12-13, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Southin (US2023/0162243) in view of Nix et al. (US2005/0071249).
As per independent Claim 1,
Southin teaches a computerized system comprising: one or more computer processors; and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations comprising: (figure 1 and para. 111 processor and memory)
receiving an item image for an item, the item image captured using a visual recognition engine that isolates damaged areas of the item (para. 113 capture images of vehicle and metadata for the images; para. 135 visual overlays that can be aligned with the vehicle to capture an image of the vehicle; para. 136 sequence of mobile optimized vehicle capture modules that set guidelines to help the user capture the vehicle; figure 2 and para. 156 capturing an image of a car and the interface provides different indicia that triggers generation of an overlay for different components of the vehicle (body, roofline, side glass, etc.) and the interface includes indicia to create a bounding box and semantic segmentation to assist the recognition engine)
generating a damage score based on the damaged areas associated with item features of the item, wherein the damage score represents a quantitative measure of a condition of the item (para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage)
identifying dynamic price analysis data associated with the item, the dynamic price analysis data comprising inputs from market data that enable dynamic pricing predictions (para. 113 process vehicle metrics to detect defects of the vehicle and compute cost data for repair of the defects and compute market value estimate using vehicle metrics and cost data; para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle can assist in establishing accurate value; para. 147 value for vehicle can be accurately generated by compiling historical transaction and advertised market listings, and crawling local advertisements and inventory on websites)
generating a predicted price for the item using a machine learning price prediction engine, wherein the machine learning price prediction engine determines the predicted price based on the damage score and the dynamic price analysis data (para. 112 the system can implement different machine learning methods for determining the condition, estimated reconditioning costs, and estimated market value for a vehicle; para. 147 market valuation tool computes estimation of the market value of the vehicle using vehicle metrics and cost data; para. 138 vehicle metrics used to populate valuation tool to deliver to the seller an accurate market value)
communicating the predicted price to cause display of the predicted price (para. 138 calculate and deliver to the seller an accurate market value; para. 113 and 180 generate an interface with visual elements corresponding to the interactive guide, market value estimate, the cost data, and at least a portion of the vehicle metrics; see also para. 140)
Southin does not teach, but Nix teaches:
the dynamic price analysis data comprising realtime inputs from continuously updated market data that enable dynamic pricing predictions (para. 64-67 where in para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good”; para. 28-33 pricing sources where in para. 30 inventory system provides current price information and sales registry provides “data on the prices at which goods have been sold in the past. Reflective of historical trends and fluctuations in the market, past prices may prove to be more or less predictive of future market prices depending on a variety of factors”; para. 31 marketplaces regularly provides pricing data to the system and the system may actively obtain pricing information using crawlers; para. 33 data provided on different cycles; para. 35 where the data is used to calculate market price for a used or new good “The market price reflects a competitive price of a good calculated based on application of an algorithm to a variety of sources of market data”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin invention with Nix with the motivation of improving the dynamic price analysis data (by using the “real-time or most recently uploaded prices”).
See para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good. A data set is selected from the various options according to the quantity of data available and a hierarchy. In an embodiment, the data set must contain a certain number of data points or otherwise reflect a large enough sample size in order to be used. In addition, data for used versions of the good may be selected over data for new prices of the good since the object is to get a market price for a used version of a good. In addition, among different sources of data for used versions of the good, data that comes from a larger market could be favored over data provided from a smaller market”. See also para. 7 and 35.
As per independent Claim 12,
Southin teaches a computer-implemented method, the computer-implemented method comprising: (figure 1 and para. 111 processor and memory)
accessing a damage score based on damage areas associated with item features of an item, wherein the item features of the item are identified from an item image captured using a visual recognition engine that isolates damaged areas of the item (para. 113 capture images of vehicle and metadata for the images; para. 135 visual overlays that can be aligned with the vehicle to capture an image of the vehicle; para. 136 sequence of mobile optimized vehicle capture modules that set guidelines to help the user capture the vehicle; figure 2 and para. 156 capturing an image of a car and the interface provides different indicia that triggers generation of an overlay for different components of the vehicle (body, roofline, side glass, etc.) and the interface includes indicia to create a bounding box and semantic segmentation to assist the recognition engine; para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage)
identifying dynamic price analysis data associated with the item, the dynamic price analysis data comprising inputs from market data that enable dynamic pricing predictions (para. 113 process vehicle metrics to detect defects of the vehicle and compute cost data for repair of the defects and compute market value estimate using vehicle metrics and cost data; para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle can assist in establishing accurate value; para. 147 value for vehicle can be accurately generated by compiling historical transaction and advertised market listings, and crawling local advertisements and inventory on websites)
generating a predicted price for the item using a machine learning price prediction engine, wherein the machine learning price prediction engine determines the predicted price based on the damage score and the dynamic price analysis data, wherein the machine learning price prediction engine enables price optimization by incorporating the damage score as a quantitative measure of a condition of the item (para. 112 the system can implement different machine learning methods for determining the condition, estimated reconditioning costs, and estimated market value for a vehicle; para. 147 market valuation tool computes estimation of the market value of the vehicle using vehicle metrics and cost data; para. 138 vehicle metrics used to populate valuation tool to deliver to the seller an accurate market value)
updating one or more interface elements associated with an adaptive pricing and inventory management interface (para. 138 calculate and deliver to the seller an accurate market value; para. 113 and 180 generate an interface with visual elements corresponding to the interactive guide, market value estimate, the cost data, and at least a portion of the vehicle metrics; see also para. 140)
Southin does not teach, but Nix teaches:
the dynamic price analysis data comprising real-time inputs from continuously updated market data that enable dynamic pricing predictions (para. 64-67 where in para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good”; para. 28-33 pricing sources where in para. 30 inventory system provides current price information and sales registry provides “data on the prices at which goods have been sold in the past. Reflective of historical trends and fluctuations in the market, past prices may prove to be more or less predictive of future market prices depending on a variety of factors”; para. 31 marketplaces regularly provides pricing data to the system and the system may actively obtain pricing information using crawlers; para. 33 data provided on different cycles; para. 35 where the data is used to calculate market price for a used or new good “The market price reflects a competitive price of a good calculated based on application of an algorithm to a variety of sources of market data”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin invention with Nix with the motivation of improving the dynamic price analysis data (by using “the real-time or most recently uploaded prices”).
See para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good. A data set is selected from the various options according to the quantity of data available and a hierarchy. In an embodiment, the data set must contain a certain number of data points or otherwise reflect a large enough sample size in order to be used. In addition, data for used versions of the good may be selected over data for new prices of the good since the object is to get a market price for a used version of a good. In addition, among different sources of data for used versions of the good, data that comes from a larger market could be favored over data provided from a smaller market”. See also para. 7 and 35.
As per independent Claim 17,
Southin teaches one or more non-transitory computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations, the operations comprising: (figure 1 and para. 111 processor and memory; para. 3, 22, 42, 248-249 non-transitory computer readable storage medium)
communicating an item image for an item, the item image captured using a visual recognition engine that isolates damaged areas of the item (para. 113 capture images of vehicle and metadata for the images; para. 135 visual overlays that can be aligned with the vehicle to capture an image of the vehicle; para. 136 sequence of mobile optimized vehicle capture modules that set guidelines to help the user capture the vehicle; figure 2 and para. 156 capturing an image of a car and the interface provides different indicia that triggers generation of an overlay for different components of the vehicle (body, roofline, side glass, etc.) and the interface includes indicia to create a bounding box and semantic segmentation to assist the recognition engine)
based on communicating the item image, receiving a predicted price, wherein the predicted price is generated for the item using a machine learning price prediction engine, (para. 112 the system can implement different machine learning methods for determining the condition, estimated reconditioning costs, and estimated market value for a vehicle; para. 147 market valuation tool computes estimation of the market value of the vehicle using vehicle metrics and cost data; para. 138 vehicle metrics used to populate valuation tool to deliver to the seller an accurate market value)
a damage score representing a quantitative measure of a condition of the item, and (para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage)
dynamic pricing analysis data comprising inputs from market data that enable dynamic pricing predictions (para. 113 process vehicle metrics to detect defects of the vehicle and compute cost data for repair of the defects and compute market value estimate using vehicle metrics and cost data; para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle can assist in establishing accurate value; para. 147 value for vehicle can be accurately generated by compiling historical transaction and advertised market listings, and crawling local advertisements and inventory on websites)
causing display of the predicted price (para. 138 calculate and deliver to the seller an accurate market value; para. 113 and 180 generate an interface with visual elements corresponding to the interactive guide, market value estimate, the cost data, and at least a portion of the vehicle metrics; see also para. 140)
Southin does not teach, but Nix teaches:
dynamic price analysis data comprising real-time inputs from continuously updated market data that enable dynamic pricing predictions (para. 64-67 where in para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good”; para. 28-33 pricing sources where in para. 30 inventory system provides current price information and sales registry provides “data on the prices at which goods have been sold in the past. Reflective of historical trends and fluctuations in the market, past prices may prove to be more or less predictive of future market prices depending on a variety of factors”; para. 31 marketplaces regularly provides pricing data to the system and the system may actively obtain pricing information using crawlers; para. 33 data provided on different cycles; para. 35 where the data is used to calculate market price for a used or new good “The market price reflects a competitive price of a good calculated based on application of an algorithm to a variety of sources of market data”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin invention with Nix with the motivation of improving the dynamic price analysis data (by using “the real-time or most recently uploaded prices”).
See para. 64 market price for a used good calculated based on data set “the real-time or most recently uploaded prices for used versions of the good offered in one or more markets, historical prices at which used versions of the good were sold, or prices of new versions of the good. A data set is selected from the various options according to the quantity of data available and a hierarchy. In an embodiment, the data set must contain a certain number of data points or otherwise reflect a large enough sample size in order to be used. In addition, data for used versions of the good may be selected over data for new prices of the good since the object is to get a market price for a used version of a good. In addition, among different sources of data for used versions of the good, data that comes from a larger market could be favored over data provided from a smaller market”. See also para. 7 and 35.
As per dependent Claim 3,
Southin/Nix teaches the system of claim 1.
Southin further teaches:
wherein generating the damage score is based on: extracting the item features associated with the item (para. 156 capture an image of a car and the interface can include indicia/buttons to identify different parts of the vehicle to be provided as metadata; see also para. 130-134)
segmenting the damage areas using image processing and segmentation techniques to isolate damage regions (figure 2 and para. 156 capturing an image of a car and the interface provides different indicia that triggers generation of an overlay for different components of the vehicle (body, roofline, side glass, etc.) and the interface includes indicia to create a bounding box and semantic segmentation to assist the recognition engine; para. 157 indicia that generate damage or defect labels to identify different defects on the vehicle)
generating the damage score using machine learning algorithms that generate standardized damage metrics for consistent assessments, (para. 112 the system can implement different machine learning methods for determining the condition, estimated reconditioning costs, and estimated market value for a vehicle; para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage; para. 117 severity of damage can affect overall evaluation of the vehicle and there is a standardized tolerance of what is considered medium, major, or impact damage type of dent; para. 128 designate the vehicle with specific industry standard grading bands)
wherein the damage score is factored into price optimization (para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle can assist in establishing accurate value)
As per dependent Claim 5 and Claim 15,
Southin/Nix teaches the system of claim 1 and the computer-implemented method of claim 12.
Southin further teaches:
wherein the price analysis data is associated with features comprising one or more of: the damage score, condition, age, real-time market demand and supply trends, regional interest and buyer preferences, seasonal factors, and historical sales and competitor pricing data (para. 113 process vehicle metrics to detect defects of the vehicle and compute cost data for repair of the defects and compute market value estimate using vehicle metrics and cost data; para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle (“damage score”) can assist in establishing accurate value; para. 147 value for vehicle can be accurately generated by compiling historical transaction and advertised market listings (“historical sales and competitor pricing data”), and crawling local advertisements and inventory on websites)
As per dependent Claim 6,
Southin/Nix teaches the system of claim 1.
Southin further teaches:
wherein the price optimization determines a competitive price by assessing how a value of the item decreases in relation to its corresponding damage score and price analysis data (para. 142-143 where in para. 143 the system is trained to calculate the estimated cost of repairs required to recondition the vehicle and averaging historical cost for equivalent repairs (“price analysis data”) and an overall grading of the vehicle that can assist in establishing an accurate value (“damage score”) and in para. 142 the user viewing the interface can understand criteria for what has impacted their vehicle’s valuation by highlighting defects and how the value was calculated; see also para. 117 and 147)
As per dependent Claim 7,
Southin/Nix teaches the system of claim 1.
Southin further teaches:
the operations further comprising refining the predicted price based on validating, adjusting, and enhancing the predicted price (para. 147 the value can be accurately generated by compiling historical transaction and advertised market listings, comparing with data captured from wholesale auctions, capturing dealer’s previous sales transactions, each appraisal validated by tracking final sale price)
wherein refining the predicted price comprises generating a predicted price insight that includes explanation information associated with the predicted price and the item (para. 142 the user viewing the interface can understand criteria for what has impacted their vehicle’s valuation by highlighting defects and how the value was calculated; para. 143 calculating the impact for less desirable vehicle attributes; para. 113 and 180 generate an interface with visual elements corresponding to the interactive guide, market value estimate, the cost data, and at least a portion of the vehicle metrics)
As per dependent Claim 13,
Southin/Nix teaches the computer-implemented method of claim 12.
Southin further teaches:
wherein the damage score is generated using machine learning algorithms that generate standardized damage metrics for consistent assessments (para. 112 the system can implement different machine learning methods for determining the condition, estimated reconditioning costs, and estimated market value for a vehicle; para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage; para. 117 severity of damage can affect overall evaluation of the vehicle and there is a standardized tolerance of what is considered medium, major, or impact damage type of dent; para. 128 designate the vehicle with specific industry standard grading bands)
As per dependent Claim 16,
Southin/Nix teaches the computer-implemented method of claim 12.
Southin further teaches:
wherein the price optimization determines a competitive price by assessing how a value of the item decreases in relation to its corresponding damage score and price analysis data (para. 142-143 where in para. 143 the system is trained to calculate the estimated cost of repairs required to recondition the vehicle and averaging historical cost for equivalent repairs (“price analysis data”) and an overall grading of the vehicle that can assist in establishing an accurate value (“damage score”) and in para. 142 the user viewing the interface can understand criteria for what has impacted their vehicle’s valuation by highlighting defects and how the value was calculated; see also para. 117 and 147)
As per dependent Claim 20,
Southin/Nix teaches the media of claim 17.
Southin further teaches:
wherein the machine learning price prediction engine enables price optimization by incorporating the damage score as a quantitative measure of a condition of the item, (para. 112 machine learning; para. 125 compute metrics that correspond to an assessment of damage; para. 126 generate grade for the vehicle; para. 128-134 where in para. 130 determines the damage/severity of each component and the table for the vehicle condition grade; para. 136 recognition engine analyzes images to determine vehicle metrics such as scratches, defects, dents listing affected body parts and severity of damage)
wherein the price optimization determines a competitive price by assessing how a value of the item decreases in relation to its corresponding damage score and price analysis data (para. 142-143 where in para. 143 the system is trained to calculate the estimated cost of repairs required to recondition the vehicle and averaging historical cost for equivalent repairs (“price analysis data”) and an overall grading of the vehicle that can assist in establishing an accurate value (“damage score”) and in para. 142 the user viewing the interface can understand criteria for what has impacted their vehicle’s valuation by highlighting defects and how the value was calculated; see also para. 117 and 147)
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Southin (US2023/0162243) in view of Nix et al. (US2005/0071249) as applied to claim 1 above, further in view of Mozzami et al. (US2020/0380584).
As per dependent Claim 8,
Southin/Nix teaches the system of claim 1.
Southin/Nix does not teach, but Mozzami teaches:
wherein causing display of the predicted prices comprises causing display of a predicted price insight associated with one or more of: a plurality of recommended prices, a condition summary, and a market trend (Figure 4A and para. 54 listing generation module identifies similar items in current and sold listings and then displays an average price of the listings, average selling time, and shipping cost to the seller; para. 55 listing generation module also indicates the (“market trend”) predicted demand of the item on the ecommerce site (high, medium, low, etc.); para. 56 seller can provide user input which would alter components such as similar items sold, average price, and demand prediction – the seller may focus the list of items based on price range (“plurality of recommended prices”))
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Mozzami with the motivation of increasing the chances of selling the listing.
See Para. 2 “Since a well-constructed listing (having accurate, complete information) will increase the chances that the associated item will sell, it would be advantageous if innovative computer technology could be employed to enhance and standardize the quality of listings.” See also para. 5-6.
As per dependent Claim 9,
Southin/Nix teaches the system of claim 1.
Southin suggests the limitation in paragraph 112, 140, and para. 243-244.
Southin/Nix does not teach, but Mozzami teaches:
wherein the machine learning price prediction engine is associated with an adaptive pricing and inventory engine that is integrated into an item listing workflow of an item listing platform (Figure 2A-F and para. 26-33 where in para. 26 machine learning technology (listing generation module and item selection module) are used to create listings and para. 27-28 where the seller is providing input to create the listing; para. 39-43 where in para. 39 a price is suggested to the seller and in para. 43 the listing is created; see also para. 49 where the suggested price is generated)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Mozzami with the motivation of increasing the chances of selling the listing.
See Para. 15 “sellers 122 may interact with a listing generation module 106 and item selection module 116 to automatically create listings 110 that are accurate, complete and of consistently high quality”.
As per dependent Claim 10,
Southin/Nix teaches the system of claim 1.
Southin/Nix does not teach, but Mozzami teaches:
wherein the item is associated with an item listing workflow, the item listing workflow supports a seller user interface interaction model that provides a plurality of item listing workflows based on user interface selections identified via a seller user interface and the predicted price (para. 12 for sale objects (FSO) may be any item the user wishes to sell via the ecommerce site; para. 14 ecommerce site includes a listing database and para. 15 sellers interact with a listing generation module and item selection module to create listings; para. 55 listing generation module also indicates the predicted demand of the item on the ecommerce site (high, medium, low, etc.); para. 56 seller can provide user input which would alter components such as similar items sold, average price, and demand prediction; figure 2F and para. 33-36 user utilizes a slider to place important on time to sale or feature matching where feature matching calculates optimum time to sale based on similarly listed items under the same categories, condition, price sold, etc.; figure 2G and para. 37-38 user can alter weights of features; figure 2H and para. 39-41 listing generation module suggests a price to the seller for pricing the seller’s FSO; para. 42-43 listing is monitored and listing is generated in figure 2I; figure 3D and para. 49 suggest a price based on current price of similar listings currently active and the actual sell price of sold listings)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Mozzami with the motivation of increasing the chances of selling the listing.
See Para. 2 “Since a well-constructed listing (having accurate, complete information) will increase the chances that the associated item will sell, it would be advantageous if innovative computer technology could be employed to enhance and standardize the quality of listings.” See also para. 5-6.
See Para. 15 “sellers 122 may interact with a listing generation module 106 and item selection module 116 to automatically create listings 110 that are accurate, complete and of consistently high quality”.
As per dependent Claim 11,
Southin/Nix teaches the system of claim 1.
Southin/Nix does not teach, but Mozzami teaches:
the operations further comprising generating a suggested action for an item listing of the item based on analyzing item listing system data associated with item listings and item sales on the item listing system (figure 2F and para. 33-36 where in para. 34 data points come from similar listings currently active and sold listings and para. 35 the listings are averaged with regards to time to sale and the time to sale is displayed, in para. 36 the user may accept the time to sale (“suggested action”); see also para. 56 and figure 4A)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Mozzami with the motivation of increasing the chances of selling the listing.
See Para. 2 “Since a well-constructed listing (having accurate, complete information) will increase the chances that the associated item will sell, it would be advantageous if innovative computer technology could be employed to enhance and standardize the quality of listings.” See also para. 5-6.
Claims 2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Southin (US2023/0162243) in view of Nix et al. (US2005/0071249) as applied to claim 1 and 17 above, further in view of Tan et al. (EP4343714).
As per dependent Claim 2 and Claim 18,
Southin/Nix teaches the system of claim 1 and the media of claim 17.
Southin teaches a visual recognition engine… segmentation of visual data based on overlaying digital information on the item to support isolating damaged areas in para. 114-118 where the cage is an overlay on the image. Southin also teaches quantifying an impact of the damaged areas on an overall condition of the item in para. 117 and 130-134.
However, Southin does not explicitly state that the visual recognition engine is an AR or MR type.
Southin/Nix does not teach, but Tan teaches:
wherein the item image for the item is captured using an Augmented Reality (AR) or Mixed Reality (MR) visual recognition engine, the AR or MR enables real-time segmentation of visual data based on overlaying digital information on the item to support isolating the damaged areas (para. 33-38 where in 33-34 augmented reality technique displaying a dynamic virtual frame superimposed on the image; para. 39 automatic image analysis for detecting damages; see also para. 45-48 and para. 99-108)
calculating dimensions (para. 18, 45, 86 where in para. 45 augmented reality techniques display data such as size, dimension, and severity of the damage)
quantifying an impact of the damaged areas on an overall condition of the item (para. 15-22 where in para. 19 calculate a numerical severity score for each identified area of damage and para. 45-46 where augmented reality techniques may be used to display data specifically the area of damage may be highlighted by a colored outline which is color coded according to severity of damage)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Tan with the motivation of guiding the user in capturing the item image. See para. 3 and para. 33 “To ensure that a user present at the site of a damaged object acquires images of the damaged object with an optimal position and from an optimal angle (e.g. to ensure that an appropriate three-dimensional model of the object can be constructed), in the step of acquiring the at least two images an augmented reality technique can be used to guide a user in taking adequate images”.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Southin (US2023/0162243) in view of Nix et al. (US2005/0071249) in view of Tan et al. (EP4343714) as applied to claim 18 above, further in view of Mozzami et al. (US2020/0380584).
As per dependent Claim 19,
Southin/Nix teaches the media of claim 18.
Southin teaches:
the operations further comprising: communicating one or more user input using an item damage assessment interface associated with the overlay digital information, wherein the digital information is associated with item damage assessment interface elements of a system
(para. 142 conversational element that can personalize the dialog with the user for exceptions and any confirmation of results or disclosures; para. 135 visual overlays that can be aligned with the vehicle to capture an image of the vehicle; para. 136 sequence of mobile optimized vehicle capture modules that set guidelines to help the user capture the vehicle; figure 2 and para. 156 capturing an image of a car and the interface provides different indicia that triggers generation of an overlay for different components of the vehicle (body, roofline, side glass, etc.) and the interface includes indicia to create a bounding box and semantic segmentation to assist the recognition engine)
Nix teaches an item listing system, but Mozzami teaches the context of [user input using an item damage assessment interface] of the item listing system.
Southin/Nix does not teach, but Mozzami teaches:
an item listing system (para. 14-16 ecommerce site that allows sellers to create listings for FSOs; figure 2E and para. 31 questions posed to the seller include request information about the condition of the item; figure 3C and para. 48-49; para. 56 seller may enter a condition of the item to focus the demand prediction on just items of that condition)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Mozzami with the motivation of allowing sellers to create listings to sell their items on the ecommerce site. See Para. 15 “sellers 122 may interact with a listing generation module 106 and item selection module 116 to automatically create listings 110 that are accurate, complete and of consistently high quality.”
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Southin (US2023/0162243) in view of Nix et al. (US2005/0071249) as applied to claim 1 and 12 above, further in view of Oh et al. (US2021/0406937).
As per dependent Claim 4 and Claim 14,
Southin/Nix teaches the system of claim 1 and the computer-implemented method of claim 12.
Southin further teaches:
wherein the price analysis data is associated with a plurality of features (para. 113 process vehicle metrics to detect defects of the vehicle and compute cost data for repair of the defects and compute market value estimate using vehicle metrics and cost data; para. 143 estimated cost of repairs using average historical cost for equivalent repairs in the market area of the user, an overall grading of the vehicle can assist in establishing accurate value; para. 147 value for vehicle can be accurately generated by compiling historical transaction and advertised market listings, and crawling local advertisements and inventory on websites)
Southin/Nix does not teach, but Oh teaches:
a plurality of features comprising continuous features, categorical features, and composite features, wherein the continuous features are normalized, the categorical features are encoded, and the composite features are computed (“continuous features” - para. 161 normalized score of listing pictures; para. 165 social favorability score may be normalized; see also para. 123-124 normalized score for nodes in comparison to previous listings; “categorical features” - para. 134 categorical data may also be parsed and encoded where the categorical data may be brand ID, category ID, shipping fee payer, and condition; “composite features” - para. 94 pricing module identifies past listings having same/similar tabulated parameters; Para. 142 computed parameters called tabulated parameters derived from calculating metrics on previous listings)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Southin/Nix invention with Oh with the motivation of improving the calculation of the predicted price.
See Para. 7 “for automatic, intelligent generation of an offer price for a FSO” and para. 9 “Generating the optimal offer price may include: identifying past listings of the previously sold FSOs that have the same or similar category of the FSO; accessing transaction information from the identified past listings; and generating the optimal price based on at least the transaction information using either a statistics based approach, or through artificial intelligence techniques such as machine learning”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tang et al. (US2021/0295243)
Taliwal et al. (US2017/0293894)
Helstab (US2018/0025392)
Rothman (US2009/0164383)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Ma whose telephone number is (571)272-2495. The examiner can normally be reached Monday to Thursday 7 AM - 5 PM.
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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628
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