DETAILED ACTION
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 .
The following FINAL Office Action is in response to Applicant’s communication filed 04/13/2026 regarding Application 18/959,146.
Status of Claim(s)
Claim(s) 1-13, and 15-21 is/are currently pending and are rejected as follows.
Response to Arguments – 101 Rejection
Applicant’s arguments and amendments with regard to the previously applied 101 rejection have been fully considered and deemed persuasive.
Examiner therefore withdraws the previously applied 101 rejection.
Response to Arguments – 102 Rejection
Applicant’s arguments with regards to the previously applied 102 rejection are rendered moot in view of the amended prior art rejection below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1-13 and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lagerling (US 2022/0067571 A1) in view of Singh (US 2021/0073894 A1)
Claim(s) 1 and 10 –
Lagerling discloses the following:
one or more processors; and (Lagerling: Paragraph 104, “Method 1200 shall be described with reference to FIGS. 1 and 4-13. However, method 1200 is not limited only to those example embodiments. The steps of method 1200 may be performed by at least one computer processor coupled to at least one memory device. An exemplary processor and memory device(s) are described below with respect to 1304 of FIG. 13. In some embodiments, method 1200 may be performed using system 100 of FIG. 1, which may further include at least one processor and memory such as those of FIG. 13.”)
a computer-readable storage medium storing instructions that are executable by the one or more processors to perform operations comprising: (Lagerling: Paragraph 139, “Computer system 1300 may also include one or more secondary storage devices or secondary memory 1310. Secondary memory 1310 may include, for example, a main storage drive 1312 and/or a removable storage device or drive 1314. Main storage drive 1312 may be a hard disk drive or solid-state drive, for example. Removable storage drive 1314 may be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup device, and/or any other storage device/drive.”)
receiving input indicating at least one item; (Lagerling: Paragraph 30, “Beyond interaction with a user (e.g., seller) at the frontend, the backend may process input from a user and/or a variety of other sources. For example, if a seller provides input regarding an item for a new listing (e.g., photographs, price ranges, or input requesting price suggestions), the backend may seek to confirm, via other input, an identification of what the seller is attempting to list. The other input may be prompted from the seller, automatically derived from other sources, or a combination of both, in some embodiments.”; Paragraph 37, “At the initial input stage, or at any subsequent input stage, system 100 may seek additional user input via the app, for example. Input may be text in the form of a character string, a photographic image, voice recognition, other characteristic sounds or audio fingerprints, for example. Text-based input via the app may prompt a user for a few fields of information depending on a broad category of the item to be listed for sale, rather than an exhaustive description of all features. In many cases, even basic text information may allow the backend to resolve SKU-level data, or at least present a few likely candidate items to a seller for confirmation of the correct item identification.”)
receiving, from one or more applications, context data associated with the at least one item, wherein the context data indicates current trends associated with the at least one item and historical user engagement data associated with the at least one item (Lagerling: Paragraph 45, “Once system 100 “understands” what an item is, such as by machine learning or other artificial intelligence, object recognition, or any other means available to system 100, additional calculations may be performed, such as to analyze and categorize the items based on other data or corresponding metadata, such as price, condition (new, used, good, fair, etc.) marketability trends, etc.”; Paragraph 127, “In some use cases, for estimation or suggestion of original or adjusted list prices in product listings for sale, a predicted value set may represent at least one trend or boundary inferred from historical data or from extrapolated regression points, and may be used to generate a prediction of an estimated sale price, a suggestion of a price likely (or more likely) to result in a sale within a predetermined time period, a prediction of whether a given price may likely result in a sale within the predetermined time period (within a given confidence interval), or a combination thereof.”)
generating, prior to generating a listing of the at least one item and based on the input and the context data, a probability that a user will engage with the listing of the at least one item (Lagerling: Paragraph 50, “In an example of a practical application of such a curve, if a given price (e.g., based on arbitrary user input when creating a listing of an item for sale) results in a prediction that a sale is unlikely (e.g., because similar items have not sold at or above the given price), a lower price may be suggested while the item is listed for sale, within a specified maximum drop, according to some implementations.”; Paragraph 51, “If a recommended drop exceeds a given threshold, for example, if the price drop is sufficiently near, at, or above the specified maximum price drop, the seller may be notified, e.g., via a push notification, that the price may be too high to sell, that the price should be dropped, that the price will be automatically dropped, and/or at that the price exceeds at least one threshold, any of which may be specified in a notification. Some examples of potential price points for notifications are shown in FIG. 2. “; Paragraph 76, “Otherwise, if the floor price is in the suggested price range based on the predicted value, but the initial listing price is still above the maximum value (710), the system may suggest to the user to drop the listing price to the maximum price of the range. This suggestion may be immediately (e.g., before or upon creating the listing), or within a predetermined amount of time following creation of the listing, e.g., six hours in this example, but other predetermined amounts may be used in other implementations (718).”; Paragraph 87, “Thus, as shown in FIG. 9 within the user interface, a price suggestion may be presented, which may correspond to a certain amount or percentage below an original list price (e.g., 15% below list price), while the user interface may still allow a user to adjust the floor price manually to another price if desired. Other messages may be displayed to the user, such as informational notices of additional costs or fees, or other warnings or suggestions if a manually entered price exceeds a threshold above or below a suggested price, for example.”; Paragraph 131, “For example, two values may define a range of prices within which a given item (e.g., input of selected object, or selection received at 1202) has at least a certain probability of selling within a given period of time (duration and/or specific dates). Separately, a different range of prices may be determined for probability of completing a sale irrespective of time. Additionally, or alternatively, according to some embodiments, a first value may represent a highest price for a given probability of sale irrespective of time, and a second value may be determined to be a highest price for a given probability of sale within a given time constraint, in some use cases. As time elapses, ranges of prices may be adjusted, as described elsewhere herein, e.g., in accordance with FIGS. 4-11.”)
displaying, based on the probability satisfying a threshold value, a control selectable to automatically generate the listing of the at least one item (Lagerling: Paragraph 51, “If a recommended drop exceeds a given threshold, for example, if the price drop is sufficiently near, at, or above the specified maximum price drop, the seller may be notified, e.g., via a push notification, that the price may be too high to sell, that the price should be dropped, that the price will be automatically dropped, and/or at that the price exceeds at least one threshold, any of which may be specified in a notification. Some examples of potential price points for notifications are shown in FIG. 2”; Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 76, “Otherwise, if the floor price is in the suggested price range based on the predicted value, but the initial listing price is still above the maximum value (710), the system may suggest to the user to drop the listing price to the maximum price of the range. This suggestion may be immediately (e.g., before or upon creating the listing), or within a predetermined amount of time following creation of the listing, e.g., six hours in this example, but other predetermined amounts may be used in other implementations (718).”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”; Paragraph 130, “Additionally, or alternatively, the output of at least part of the predicted-value set may be a quantitative difference between a predicted value (or composite value thereof) and a predetermined threshold, such as by how much the predetermined threshold value is different from the predicted value or composite value thereof. Thus, according to some embodiments, the output of at least part of the predicted-value set may be or include a suggestion of a value within an expected range of values (e.g., determined by the ML-based estimation of 1206) and/or recommendation including a value or expected range of values for which the selected object may be better suited (e.g., determined by the confidence condition of 1208, a price at which the selected object is more likely to sell).”)
Lagerling does not explicitly disclose the following, however, in analogous art of marketplace listing and generation, Singh discloses the following:
and automatically generating, based on a selection at the control, the listing of the at least one item by completing a plurality of fields of the listing of the at least one item using the input and the context data.(Singh: Paragraph 12, “Frequently, purchasers of a product would like to dispose of the product by reselling it before the product has no value or has otherwise diminished in value to such an extent that it is difficult to sell to a new owner. As described above, unfortunately, in many situations, product owners are left with no viable option other than to dispose of a product for little to no value. Embodiments described herein provide systems and methods which allow product owners to identify desirable points in time at which to sell such products for value as well as to reduce the complexity of listing such products for resale. Some embodiments described herein provide systems and methods which allow product owners to specify one or more listing rules that must be met in order for a product to be listed for resale and once met, will cause a product listing to be created substantially automatically.”; Paragraph 26, “In general, user device 140 may be operated by a user who purchases one or more items 110 at a point of sale 120 (e.g., such as “John” in the above illustrative example or the “seller”). User device 140 may be operated to capture or receive information from the point of sale 120 for use in creating an item record in an item database 134 associated with listings platform 130. An illustrative user interface showing a user device 140 and a user interface 242 displaying item data from a point of sale 120 which may be used to create an item record is shown in FIG. 2 User device 140 may also be operated by a user to receive information from the listings platform 130 such as information identifying time(s) when it may be desirable to create an item listing in listings database 136 to offer the item 110 for resale. For example, user device 140 may provide a user interface that presents a user with one or more screens that allows the user to set one or more listing rules that govern when a listing should be published for the item 110. A number of different rules may be created to allow a user to control how and when an item will be listed. For example, a user may set a price target as the main rule that must be satisfied before the item will be listed for sale in a classifieds platform.”; Paragraph 33, “Further details of features of some embodiments will now be described by referring to several flow diagrams that describe processes that may be performed by an application performed by a computing device or a group of computing devices, such as a user device, a web server, a host platform, a cloud computing environment, and the like to implement features of the present invention. Referring to FIG. 5, a process 500 for operating a listings platform to list an item is shown. The process 500 may be performed by platform, such as the listings platform 130 of FIG. 1 interacting with a user device 140 and one or more third party information services 150. In general, the process 500 may begin when (or after) a user purchases an item 110. Process 500 begins at 502 where one or more item data records are created. Item data records may be created in a number of ways. For example, in some embodiments, an item data record may be created substantially automatically in response to a transaction at a point of sale. For example, a point of sale device may be configured to prompt a buyer whether the buyer wishes to create an item data record using information from the point of sale device. The data may be transmitted to listings platform 130 for storage in an item database 134 with information identifying the buyer. As another example, the item data record may be created by a user operating a user device 140. The user device 140 may initiate the creation of the item data record based on, for example, an electronic (or other) receipt received from the point of sale 120 or based on information entered or otherwise captured by the user.”; Paragraph 35, “FIG. 6 depicts a table that represents the item database 134 that may be stored at or accessible to the listings platform 130 according to some embodiments. The table may include, for example, entries identifying items purchased by users of the listings platform 130. The table may also define fields 602, 604, 606, 608, 610 for each of the entries. The fields 602, 604, 606, 608, 610 may, according to some embodiments, specify: an item identifier 602, an associated user identifier 604, an item reference 606, a description 608, and one or more listing rule(s) 610. The item database 600 may be created and updated, for example, based on information received from a buyer and/or that is automatically obtained from a point of sale 120.”; Paragraph 37, “The description 608 includes information describing the item identified by item identifier 602. The description 608 may be entered by a user, or it may be automatically obtained from a source such as a third party information service 150 based on the item reference 606 (for example, a SKU may be used to retrieve a description of the item). Other attributes of the purchase may also be provided in the description 608 (or in other fields of the database). For example, the purchase price, date and location may be provided.”)
Lagerling discloses a method for making suggestions to improve the probability of an item selling. Singh discloses a method for automatically generating a listing for an item according to user defined criteria and rules. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Lagerling with the teachings of Singh in order to improve the value a seller receives for their items while lowering the amount of effort required to do so as disclosed by Singh (Singh: Paragraph 22, “The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort.”)
Claim(s) 2 –
Lagerling in view of Singh disclose the limitations of claim 1
Lagerling further discloses the following:
obtaining additional context data corresponding to respective contexts associated with a plurality of items comprising the at least one item; and (Lagerling: Paragraph 43, “Image-based object recognition here may also leverage machine learning, in some embodiments. Systems for image-based object recognition (which may be referred to as “image recognition” in some cases) may be trained using any of backend database listings, feed data of data partners, scraped listings from public sources, or any other source of accurate training data, for example.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”; Paragraph 111, “Irrespective of how the object-identification module may be implemented, the object identification performed by the object-identification module may be configured to associate a given object (from input data, e.g., photograph, description, sound, etc.) with at least one identifier. An identifier may include any of various physical or descriptive characteristics. Other representations, e.g., vectors or arrays of data points of certain types, may be used for mapping and/or evaluating characteristic data in a way suitable for processing, e.g., with neural networks, according to some embodiments. One or more identifiers of a selected object, e.g., descriptions, may be cross-referenced with a known unique identifier, e.g., SKU or similar ID number, corresponding to a given object, for example. Cross-referencing may be implemented as indexing, dictionary lookup, relational operation, etc.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”)
training, using the additional context data, at least one learning model to output the probability based on the input and the context data. (Lagerling: Paragraph 43, “Image-based object recognition here may also leverage machine learning, in some embodiments. Systems for image-based object recognition (which may be referred to as “image recognition” in some cases) may be trained using any of backend database listings, feed data of data partners, scraped listings from public sources, or any other source of accurate training data, for example.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”; Paragraph 111, “Irrespective of how the object-identification module may be implemented, the object identification performed by the object-identification module may be configured to associate a given object (from input data, e.g., photograph, description, sound, etc.) with at least one identifier. An identifier may include any of various physical or descriptive characteristics. Other representations, e.g., vectors or arrays of data points of certain types, may be used for mapping and/or evaluating characteristic data in a way suitable for processing, e.g., with neural networks, according to some embodiments. One or more identifiers of a selected object, e.g., descriptions, may be cross-referenced with a known unique identifier, e.g., SKU or similar ID number, corresponding to a given object, for example. Cross-referencing may be implemented as indexing, dictionary lookup, relational operation, etc.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”)
Claim(s) 3 –
Lagerling in view of Singh disclose the limitations of claims 1-2
Lagerling further discloses the following:
obtaining updated data corresponding to an updated context associated with the at least one item, wherein the updated data corresponds to the user engaging with the listing of the at least one item; and (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
retraining, using the updated data, the at least one learning model to output an updated probability of that user will engage with the listing of the at least one item based on the input and the updated context associated with the at least one item. (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
Claim(s) 4 –
Lagerling in view of Singh discloses the limitations of claim 1
Lagerling does not explicitly disclose the following, however, in analogous art of marketplace listing and generation, Singh discloses the following:
displaying, based on automatically generating the listing of the at least one item, the listing of the at least one item with an additional control selectable to publish the listing of the at least one item. (Singh: Paragraph 22, “Prior to discussing details of features of the invention, for purposes of illustrating features of some embodiments of the present invention, a simple example will now be introduced and referenced throughout the disclosure. In the illustrative example, a customer (named “John”) is purchasing a new television from a retail store. John knows that he may not keep the television forever and that he may someday want to sell it. At the time of purchase at the retail store, John may interact with the system of the present invention to pre-configure a listing to resell the television using a classifieds platform. Information about the television (including, for example, a SKU or other identifier that can be used to specifically identify the make and model of the television, the purchase price, and the purchase date and location—collectively, the “item data”) are transmitted to a listings platform for storage in a record associated with an account John has with the listings platform. John can also pre-configure one or more listing rules about how or when he will list the television for resale in the future. For example, John may specify that he wants to list the television for resale after 1 year if there is a high probability that he can resell the television for at least 50% of the original purchase price. John then takes his new television home and enjoys it. Over a year later, John receives a notification from the listings platform (based on the rules that John established earlier) that there is strong demand for his make and model of television and that he will likely be able to resell the television for over 50% of the original purchase price. John responds to the notification confirming that he wishes the listings platform to proceed with a listing to attempt to sell the television. In response, the listings platform automatically posts a listing for the television including details of the television (such as the television's make, model, age and offer price) without any further intervention from John. The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort. Users do not need to be experts in pricing or marketing, as embodiments analyze market conditions to identify appropriate timing and pricing to increase the likelihood of a product being sold.”; Paragraph 32, “While in some embodiments, user devices 140 are described as computing devices (such as mobile devices, laptop computers, tablet computers, or the like) that are connected to the Internet, in other embodiments, some of the user devices 140 may be configured to send and receive information without accessing the Internet. For example, some user devices 140 may be a radio receiver issued or owned by a user. For example, the radio receiver may be an FM radio which can display some information on its display in addition to playing music or other information. Each radio receiver operating as a user device 140 may be assigned a unique identifier and such unique identifier may be stored in association with the item data. In some embodiments, a radio receiver operating as a user device 140 may receive information from listings platform 130 when an item of the user as been identified as ready to be converted into a listing as described herein. For example, a message may be transmitted to the user's radio receiver indicating that an item of the user is currently sellable. In some embodiments, the radio receiver is also configured to allow the user to transmit a message to the listings platform 130 confirming that the item data should be converted to a listing. In some embodiments, the radio receiver may be connected to the item through Bluetooth or other wireless connection and upon receipt of a message from the listings platform 130, may cause the item (or a device associated with the item) to glow ambiently. Information about the potential listing may also be displayed (such as, for example, the amount at which it could be sold and the degree of demand as will be described further herein).”; Paragraph 47, “If processing at 506 indicates that one or more listing rules associated with an item have been met (i.e., current conditions satisfy the listing rule(s)), processing continues at 508 where the listings platform 130 is operated to inform the user that listing rule(s) have been met. The user may be informed via an email, text message or some other notification transmitted to the user's user device 140. The notification may include information about the specific item as well as information about the asking price recommended by the listings platform 130 as well as information about the relative degree of demand for the item in the user's geographical area (e.g., such as medium, high or very high). If the user wishes to confirm the listing, he may simply respond to the notification or click a button or link associated with the notification. Alternatively, if the user does not wish to confirm the listing, he may simply ignore the notification (in which case the transaction will cancel or expire) or respond to the notification or click a decline button or link associated with the notification. If the user declines, processing reverts to 506 and the listings platform 130 continues to monitor conditions to identify further rule compliance.”)
Lagerling discloses a method for making suggestions to improve the probability of an item selling. Singh discloses a method for automatically generating a listing for an item according to user defined criteria and rules. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Lagerling with the teachings of Singh in order to improve the value a seller receives for their items while lowering the amount of effort required to do so as disclosed by Singh (Singh: Paragraph 22, “The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort.”)
Claim(s) 5 –
Lagerling in view of Singh discloses the limitations of claims 1 and 4
Lagerling further discloses the following:
obtaining an output from at least one learning model that indicates information associated with the listing of the at least one item, wherein the information comprises one or more of a description of the at least one item, a category of the at least one item, a value of the at least one item, or a condition of the at least one item; (Lagerling: Paragraph 60, “Additionally, or alternatively, output may include a qualitative suggestion, e.g., that the sale may be slow or may not happen, and/or a general indication that a lower price may be more likely to sell or be viewed by more potential buyers, with or without a specific value or range of values for suggested price. In some embodiments, a frequency chart or probability density curve may be displayed, in addition to, or instead of, any of the above outputs. If, on the other hand, a higher price is estimated to receive more views, some embodiments may provide a suggestion to raise the price to increase proceeds and/or to close a sale even more quickly, for example.”; Paragraph 89, “Additionally, or alternatively, timing and/or rate (absolute amount or relative percentage) of price drops may be determined based at least in part on item metadata, which may include a category or description of the item in the listing, or dynamic counts of page views, search hits, or other interest in the particular listing of the item or of other items similar to the item in the particular listing. Thus, items in lower-volume or less-popular categories may have larger price drops recommended to offset low demand, whereas target categories in a high-demand market may increase likelihood of sales following relatively smaller price drops by comparison.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”)
and automatically completing, using the information, the plurality of fields, wherein the plurality of fields are associated with a configurable template for the listing of the at least one item. (Lagerling: Paragraph 21, “In this non-limiting example embodiment, data in automatically created table 130 may include rows, columns, keys, elements, etc., as understood with other database tables. A column or key may correspond to a unique identifier (UID) of a product, such as may be a stock-keeping unit (SKU), for example, accompanied by other fields, columns, keys, elements, etc., corresponding to prices, statistics, conditions (new, used, good, fair, etc.), or other metrics. For example, there may be separate columns for each of first quartile (Q.sub.1), median (Q.sub.2), third quartile (Q.sub.3), or other composite statistics of cleared sale prices corresponding to a given unique identifier within a given time period, according to some embodiments. Similar corresponding schemata and data may also be present in a manually created table 132, as shown on the right-hand side.”; Paragraph 37, “At the initial input stage, or at any subsequent input stage, system 100 may seek additional user input via the app, for example. Input may be text in the form of a character string, a photographic image, voice recognition, other characteristic sounds or audio fingerprints, for example. Text-based input via the app may prompt a user for a few fields of information depending on a broad category of the item to be listed for sale, rather than an exhaustive description of all features. In many cases, even basic text information may allow the backend to resolve SKU-level data, or at least present a few likely candidate items to a seller for confirmation of the correct item identification.”; Paragraph 113, “For example, a known unique identifier may be any of a uniform, universal, and/or unique identifier, including but not limited to a stock-keeping unit (SKU), universal product code (UPC), uniform resource identifier (URI), uniform resource locator (URL), uniform resource name (URN), international standard book number (ISBN), Amazon standard item number (ASIN), document identifier, etc., to which a given item may be mapped, in some non-limiting example embodiments. Additionally or alternatively, a value may include a checksum, fingerprint, signature, digest, hash, cryptographic hash, etc., corresponding to at least one of the first input or second input (e.g., character string item description, specific text field for brand name, enumerated selector for size, model year, etc.), to track inputs and outputs and/or to determine matches or duplicates, for example.”)
Claim(s) 6 –
Lagerling in view of Singh discloses the limitations of claim 1
Lagerling further discloses the following:
obtaining one or more performance metrics associated with the listing of the at least one item, wherein the one or more performance metrics comprise one or more of click- through rates, conversion rates, average time spent viewing listings, number of user inquiries, or user engagement volume for one or more additional items associated with the at least one item; (Lagerling: Paragraph 22, “Based at least in part on processing these values, and possibly other platform statistics or performance indicators, a value may be estimated for a prediction or recommendation, which may correspond to a suggested price, in some embodiments. Additionally, certain other values may be generated, such as a minimum and/or maximum value defining a range of values around to the estimated value, thus corresponding to a predicted or recommended range of suggested prices generated from the estimated or predicted value, according to further embodiments.”; Paragraph 27, “Again discussing 118, if execution has flowed to ML-based estimation algorithm(s) 120 for a particular output of object identification module 104, an estimation (prediction, suggestion, etc.) may be performed. To compensate for any potential unreliability in ML-based estimation algorithm(s) 120, a predetermined confidence condition 122 may be checked, to determine whether the predicted value or predicted-value set satisfies the predetermined confidence condition 122. If so, a value may be returned at 124 and end 128 execution of a given function or method for a given output of object identification module 104. If the confidence condition is not satisfied, ML-based estimation algorithm(s) 120 may return NULL at 126. Additionally, or alternatively, a range of values may be generated around the returned value 116. In other embodiments (not shown), a NULL return from 114-116 may result in proceeding to another estimation module, such as a statistics-based estimation module (not shown), for example.”; Paragraph 59, “Moreover, similar to the notifications as described with respect to FIG. 2, if a price has not been automatically lowered after a number of views has peaked (e.g., at a point of “most views” as shown in FIG. 3) and has declined beyond a threshold, a seller may be prompted to lower the price (or to activate a smart-pricing feature such that price is automatically lowered), with the expected benefit of increasing views and potentially increasing likelihood of a sale, according to some embodiments.”; Paragraph 65, “The logic flow diagram 500 shows when, for some embodiments, a promotion factor may be adjusted based on proximity of an existing listing price to a range of prices, such as suggested prices. For example, if a user-provided listing price is in a range that would have been suggested, e.g., for the same or similar items as listed, at which a sale is relatively likely to occur quickly, a listing platform may promote (e.g., increase a likelihood of hits in a search engine or frequency of recommendations of similar products, etc.) the listed item by a given factor, e.g., 3. This may happen if a normalized price is within 10% of a mean value of a range of suggested prices, as shown in FIG. 5. Other degrees of promotion factors (e.g., 2 or 1, etc.) may be provided within different or wider ranges from the mean value. In some use cases, promotion may be a reward for a user who accepts a suggested price from the listing platform, which may further reduce time to sale, likely leading to favorable engagement of the user with the platform.”)
training, based on the one or more performance metrics, a learning model to output the threshold value; and (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
obtaining, as output from the learning model, the threshold value. (Lagerling: Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 75, “For example, if the list price and floor price are both above the maximum value of the range based on the predicted value (708), it may be assumed that the user listing the item is not heeding the suggestions provided by the system in the interest of increasing the likelihood of a sale within a reasonable time, and the system may therefore evaluate whether to provide any promotion to the listing, given that the listing may have an unreasonably high price (716).”)
Claim(s) 7 –
Lagerling in view of Singh discloses the limitations of claim 1
Lagerling further discloses the following:
further comprising receiving additional input indicating the threshold value. (Lagerling: Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 75, “For example, if the list price and floor price are both above the maximum value of the range based on the predicted value (708), it may be assumed that the user listing the item is not heeding the suggestions provided by the system in the interest of increasing the likelihood of a sale within a reasonable time, and the system may therefore evaluate whether to provide any promotion to the listing, given that the listing may have an unreasonably high price (716).”)
Claim(s) 8 –
Lagerling in view of Singh discloses the limitations of claim 1
Lagerling further discloses the following:
wherein the input comprises one or more images of the at least one item. (Lagerling: Paragraph 37, “At the initial input stage, or at any subsequent input stage, system 100 may seek additional user input via the app, for example. Input may be text in the form of a character string, a photographic image, voice recognition, other characteristic sounds or audio fingerprints, for example. Text-based input via the app may prompt a user for a few fields of information depending on a broad category of the item to be listed for sale, rather than an exhaustive description of all features. In many cases, even basic text information may allow the backend to resolve SKU-level data, or at least present a few likely candidate items to a seller for confirmation of the correct item identification.”; Paragraph 40, “In a case of image input, a user attempting to create a new listing for sale may be prompted to take at least one photograph of the item to be listed. Photographic image data from the photograph(s) may be processed locally on the device running the app, or may be transmitted to the backend or to a third party for initial processing and/or further processing. Processing of the photographic image data may include use of artificial intelligence. Processing of the image data may include feeding the image data into a neural network, perceptron, or classifier, for example. The image data may be processed using computer-implemented image recognition, such as using at least one computer vision algorithm, in some embodiments. Any of the above technologies or their equivalents may be used to perform operations such as classification, object recognition and/or reverse image-searching, to name a few non-limiting examples.”)
Claim(s) 9 –
Lagerling in view of Singh discloses the limitations of claim 1
Lagerling further discloses the following:
wherein the context associated is based on one or more of an average value associated with the at least one item, an average value associated with one or more items related to the at least one item, a volume of user engagement with the at least one item, or a volume of user engagement with the one or more items related to the at least one item. (Lagerling: Paragraph 69, “After a particular time interval has elapsed (608), at least one of several other actions may be taken. For example, the particular time interval may be an average time between listing and sale (avg_sold_time) for a given item or similar item(s), or for a given category-and-brand pair, etc. The particular time interval may be a scaled factor of average time to sale, for example. Actions to be taken may include dropping the listing price by predetermined gradual steps, e.g., 5% per step (612). Another action that may be taken may include dividing the difference between LP and FP into a predetermined number of steps (610), e.g., three steps, and dropping the price gradually by each step uniformly as a fraction of the difference between LP and FP, according to some embodiments.”; Paragraph 71, “The logic flow diagram 700 shows an example of programmatic price reduction, having some elements in common with logic flow diagram 600 of FIG. 6 (compare 702 to 604, 714 to 606, 720 to 608, and 724 to 610 and 612), but adding additional checks for ranges of values into which various user-entered prices may be found with respect to suggested prices, and accounting for possible user-entered floor price. Additionally, logic flow diagram 700 may provide for additional flexibility on timing, e.g., timing offset(s) (718), for when programmatic price drops may occur, before waiting for the time since listing (age of the listed value, e.g., time since the list price was last reduced) to exceed average time to sale (and repeating in intervals of average time to sale or multiples thereof), for example.”; Paragraph 124, “In 1210, processor 1304 may output at least part of the predicted-value set. The predicted-value set may include or indicate a range of values, for example, at different confidence intervals, or at different statistical frequencies, according to some embodiments. Ranges of values may be defined by orders, series, rankings, representative values, or composite statistical measures such as minimum, maximum, median, quartiles, or quartile ranges (e.g., interquartile range, minimum to first quartile, first quartile to median, median to third quartile, third quartile to maximum, etc.), for example.”)
Claim(s) 11 –
Lagerling discloses the following:
receiving input indicating at least one item; (Lagerling: Paragraph 30, “Beyond interaction with a user (e.g., seller) at the frontend, the backend may process input from a user and/or a variety of other sources. For example, if a seller provides input regarding an item for a new listing (e.g., photographs, price ranges, or input requesting price suggestions), the backend may seek to confirm, via other input, an identification of what the seller is attempting to list. The other input may be prompted from the seller, automatically derived from other sources, or a combination of both, in some embodiments.”; Paragraph 37, “At the initial input stage, or at any subsequent input stage, system 100 may seek additional user input via the app, for example. Input may be text in the form of a character string, a photographic image, voice recognition, other characteristic sounds or audio fingerprints, for example. Text-based input via the app may prompt a user for a few fields of information depending on a broad category of the item to be listed for sale, rather than an exhaustive description of all features. In many cases, even basic text information may allow the backend to resolve SKU-level data, or at least present a few likely candidate items to a seller for confirmation of the correct item identification.”)
receiving, from one or more applications, context data associated with the at least one item, wherein the context data indicates current trends associated with the at least one item and historical user engagement data associated with the at least one item (Lagerling: Paragraph 45, “Once system 100 “understands” what an item is, such as by machine learning or other artificial intelligence, object recognition, or any other means available to system 100, additional calculations may be performed, such as to analyze and categorize the items based on other data or corresponding metadata, such as price, condition (new, used, good, fair, etc.) marketability trends, etc.”; Paragraph 127, “In some use cases, for estimation or suggestion of original or adjusted list prices in product listings for sale, a predicted value set may represent at least one trend or boundary inferred from historical data or from extrapolated regression points, and may be used to generate a prediction of an estimated sale price, a suggestion of a price likely (or more likely) to result in a sale within a predetermined time period, a prediction of whether a given price may likely result in a sale within the predetermined time period (within a given confidence interval), or a combination thereof.”)
generating, prior to generating a listing of the at least one item and based on the input and the context data, a probability that a user will engage with the listing of the at least one item (Lagerling: Paragraph 50, “In an example of a practical application of such a curve, if a given price (e.g., based on arbitrary user input when creating a listing of an item for sale) results in a prediction that a sale is unlikely (e.g., because similar items have not sold at or above the given price), a lower price may be suggested while the item is listed for sale, within a specified maximum drop, according to some implementations.”; Paragraph 51, “If a recommended drop exceeds a given threshold, for example, if the price drop is sufficiently near, at, or above the specified maximum price drop, the seller may be notified, e.g., via a push notification, that the price may be too high to sell, that the price should be dropped, that the price will be automatically dropped, and/or at that the price exceeds at least one threshold, any of which may be specified in a notification. Some examples of potential price points for notifications are shown in FIG. 2. “; Paragraph 76, “Otherwise, if the floor price is in the suggested price range based on the predicted value, but the initial listing price is still above the maximum value (710), the system may suggest to the user to drop the listing price to the maximum price of the range. This suggestion may be immediately (e.g., before or upon creating the listing), or within a predetermined amount of time following creation of the listing, e.g., six hours in this example, but other predetermined amounts may be used in other implementations (718).”; Paragraph 87, “Thus, as shown in FIG. 9 within the user interface, a price suggestion may be presented, which may correspond to a certain amount or percentage below an original list price (e.g., 15% below list price), while the user interface may still allow a user to adjust the floor price manually to another price if desired. Other messages may be displayed to the user, such as informational notices of additional costs or fees, or other warnings or suggestions if a manually entered price exceeds a threshold above or below a suggested price, for example.”; Paragraph 131, “For example, two values may define a range of prices within which a given item (e.g., input of selected object, or selection received at 1202) has at least a certain probability of selling within a given period of time (duration and/or specific dates). Separately, a different range of prices may be determined for probability of completing a sale irrespective of time. Additionally, or alternatively, according to some embodiments, a first value may represent a highest price for a given probability of sale irrespective of time, and a second value may be determined to be a highest price for a given probability of sale within a given time constraint, in some use cases. As time elapses, ranges of prices may be adjusted, as described elsewhere herein, e.g., in accordance with FIGS. 4-11.”)
displaying, based on the probability failing to satisfy a threshold value, a control selectable to generate a configurable template for the listing of the at least one item. (Lagerling: Paragraph 51, “If a recommended drop exceeds a given threshold, for example, if the price drop is sufficiently near, at, or above the specified maximum price drop, the seller may be notified, e.g., via a push notification, that the price may be too high to sell, that the price should be dropped, that the price will be automatically dropped, and/or at that the price exceeds at least one threshold, any of which may be specified in a notification. Some examples of potential price points for notifications are shown in FIG. 2”; Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 76, “Otherwise, if the floor price is in the suggested price range based on the predicted value, but the initial listing price is still above the maximum value (710), the system may suggest to the user to drop the listing price to the maximum price of the range. This suggestion may be immediately (e.g., before or upon creating the listing), or within a predetermined amount of time following creation of the listing, e.g., six hours in this example, but other predetermined amounts may be used in other implementations (718).”; Paragraph 76, “Otherwise, if the floor price is in the suggested price range based on the predicted value, but the initial listing price is still above the maximum value (710), the system may suggest to the user to drop the listing price to the maximum price of the range. This suggestion may be immediately (e.g., before or upon creating the listing), or within a predetermined amount of time following creation of the listing, e.g., six hours in this example, but other predetermined amounts may be used in other implementations (718).”; Paragraph 87, “Thus, as shown in FIG. 9 within the user interface, a price suggestion may be presented, which may correspond to a certain amount or percentage below an original list price (e.g., 15% below list price), while the user interface may still allow a user to adjust the floor price manually to another price if desired. Other messages may be displayed to the user, such as informational notices of additional costs or fees, or other warnings or suggestions if a manually entered price exceeds a threshold above or below a suggested price, for example.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”; Paragraph 130, “Additionally, or alternatively, the output of at least part of the predicted-value set may be a quantitative difference between a predicted value (or composite value thereof) and a predetermined threshold, such as by how much the predetermined threshold value is different from the predicted value or composite value thereof. Thus, according to some embodiments, the output of at least part of the predicted-value set may be or include a suggestion of a value within an expected range of values (e.g., determined by the ML-based estimation of 1206) and/or recommendation including a value or expected range of values for which the selected object may be better suited (e.g., determined by the confidence condition of 1208, a price at which the selected object is more likely to sell).”)
Lagerling does not explicitly disclose the following, however, in analogous art of marketplace listing and generation, Singh discloses the following:
and generating, based on a selection at the control, the configurable template for the listing of the at least one item, wherein the configurable template comprises a plurality of fields for manual completion using the input. (Singh: Paragraph 32, “While in some embodiments, user devices 140 are described as computing devices (such as mobile devices, laptop computers, tablet computers, or the like) that are connected to the Internet, in other embodiments, some of the user devices 140 may be configured to send and receive information without accessing the Internet. For example, some user devices 140 may be a radio receiver issued or owned by a user. For example, the radio receiver may be an FM radio which can display some information on its display in addition to playing music or other information. Each radio receiver operating as a user device 140 may be assigned a unique identifier and such unique identifier may be stored in association with the item data. In some embodiments, a radio receiver operating as a user device 140 may receive information from listings platform 130 when an item of the user as been identified as ready to be converted into a listing as described herein. For example, a message may be transmitted to the user's radio receiver indicating that an item of the user is currently sellable. In some embodiments, the radio receiver is also configured to allow the user to transmit a message to the listings platform 130 confirming that the item data should be converted to a listing. In some embodiments, the radio receiver may be connected to the item through Bluetooth or other wireless connection and upon receipt of a message from the listings platform 130, may cause the item (or a device associated with the item) to glow ambiently. Information about the potential listing may also be displayed (such as, for example, the amount at which it could be sold and the degree of demand as will be described further herein).”; Paragraph 52, “The listing description 808 may include information that will form the basis of the classifieds posting or listing that is made available to prospective buyers who interact with the listings platform 130 (e.g., via user devices 160). Embodiments ensure that accurate information is included in the listing by using details from the item database 134 about the product. For example, in some embodiments a SKU (from the item reference 606 of FIG. 6, for example) may be used to retrieve a full product description (and even a picture if desired) from the manufacturer's website or other trusted product information services. Further, links may be formed and included which allow a prospective buyer to view rating or performance data associated with the product. In some embodiments, once the listing has been created, the seller may be given an opportunity to edit the listing description 808 (e.g., to include photos of the actual item or to provide other informative descriptive information).”)
Lagerling discloses a method for making suggestions to improve the probability of an item selling. Singh discloses a method for automatically generating a listing for an item according to user defined criteria and rules. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Lagerling with the teachings of Singh in order to improve the value a seller receives for their items while lowering the amount of effort required to do so as disclosed by Singh (Singh: Paragraph 22, “The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort.”)
Claim(s) 12 –
Lagerling in view of Singh discloses the limitations of claim 11
Lagerling further discloses the following:
obtaining additional context data corresponding to respective contexts associated with a plurality of items comprising the at least one item; and (Lagerling: Paragraph 43, “Image-based object recognition here may also leverage machine learning, in some embodiments. Systems for image-based object recognition (which may be referred to as “image recognition” in some cases) may be trained using any of backend database listings, feed data of data partners, scraped listings from public sources, or any other source of accurate training data, for example.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”; Paragraph 111, “Irrespective of how the object-identification module may be implemented, the object identification performed by the object-identification module may be configured to associate a given object (from input data, e.g., photograph, description, sound, etc.) with at least one identifier. An identifier may include any of various physical or descriptive characteristics. Other representations, e.g., vectors or arrays of data points of certain types, may be used for mapping and/or evaluating characteristic data in a way suitable for processing, e.g., with neural networks, according to some embodiments. One or more identifiers of a selected object, e.g., descriptions, may be cross-referenced with a known unique identifier, e.g., SKU or similar ID number, corresponding to a given object, for example. Cross-referencing may be implemented as indexing, dictionary lookup, relational operation, etc.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”)
training, using the additional context data, at least one learning model to output the probability based on the input and the context data. (Lagerling: Paragraph 43, “Image-based object recognition here may also leverage machine learning, in some embodiments. Systems for image-based object recognition (which may be referred to as “image recognition” in some cases) may be trained using any of backend database listings, feed data of data partners, scraped listings from public sources, or any other source of accurate training data, for example.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”; Paragraph 111, “Irrespective of how the object-identification module may be implemented, the object identification performed by the object-identification module may be configured to associate a given object (from input data, e.g., photograph, description, sound, etc.) with at least one identifier. An identifier may include any of various physical or descriptive characteristics. Other representations, e.g., vectors or arrays of data points of certain types, may be used for mapping and/or evaluating characteristic data in a way suitable for processing, e.g., with neural networks, according to some embodiments. One or more identifiers of a selected object, e.g., descriptions, may be cross-referenced with a known unique identifier, e.g., SKU or similar ID number, corresponding to a given object, for example. Cross-referencing may be implemented as indexing, dictionary lookup, relational operation, etc.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”)
Claim(s) 13 –
Lagerling in view of Singh discloses the limitations of claim 11
Lagerling further discloses the following:
obtaining updated data corresponding to an updated context associated with the at least one item, wherein the updated data corresponds to the user engaging with the listing of the at least one item; and (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
retraining, using the updated data, the at least one learning model to output an updated probability of that user will engage with the listing of the at least one item based on the input and the updated context associated with the at least one item. (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
Claim(s) 15 –
Lagerling in view of Singh discloses the limitations of claims 11
Lagerling further discloses the following:
obtaining additional input that indicates information associated with the listing of the at least one item, wherein the information comprises one or more of a description of the at least one item, a category of the at least one item, a value of the at least one item, or a condition of the at least one item; (Lagerling: Paragraph 60, “Additionally, or alternatively, output may include a qualitative suggestion, e.g., that the sale may be slow or may not happen, and/or a general indication that a lower price may be more likely to sell or be viewed by more potential buyers, with or without a specific value or range of values for suggested price. In some embodiments, a frequency chart or probability density curve may be displayed, in addition to, or instead of, any of the above outputs. If, on the other hand, a higher price is estimated to receive more views, some embodiments may provide a suggestion to raise the price to increase proceeds and/or to close a sale even more quickly, for example.”; Paragraph 89, “Additionally, or alternatively, timing and/or rate (absolute amount or relative percentage) of price drops may be determined based at least in part on item metadata, which may include a category or description of the item in the listing, or dynamic counts of page views, search hits, or other interest in the particular listing of the item or of other items similar to the item in the particular listing. Thus, items in lower-volume or less-popular categories may have larger price drops recommended to offset low demand, whereas target categories in a high-demand market may increase likelihood of sales following relatively smaller price drops by comparison.”; Paragraph 92, “For inputs at a training stage, such as with machine learning or equivalent technologies, for example, a sold price 1016 such as from a data set of historical sold prices of actual items used as samples in a training set may be used for training and testing with respect to the at least one regression model and/or loss function for certain predictions or estimations of values, specifically prices, in this example. As input data for the training, a training set may include text data 1002 (e.g., description, title, brand name, category name, etc.), categorical data 1004 (e.g., brand ID, category ID, shipping fee payer, etc.), numerical data 1006 (dates, date ranges, other conditions, etc.), other such data, related data, metadata, or any combination of the above types or instances of training data for a given use case.”)
and displaying the listing of the at least one item with an additional control selectable to publish the listing of the at least one item. (Lagerling: Paragraph 79, “Flow diagram 800 represents an example use case in which a user lists a particular item (e.g., iPhone X) for sale (802) on a given platform. The user may be prompted to enter a price or range of prices, e.g., listing price (804) and floor price (806). A suggested price range may be computed and optionally presented to the user. For purposes of the example flow diagram 800, these prices may be entered, computed, and/or presented at t=0 (time at or before creation of an actual listing for sale). Once the listing has been published at t=0, this begins incrementing t with time. For the listed item or corresponding category, brand, category-and-brand pair, etc., of similar items (e.g., Apple smartphones), an average time to sell (avg_sold_time) may be calculated at 24 hours. This calculation may be based on moving-average analysis of historical data on the same platform and/or on other platforms, for example.”; Paragraph 106, “In some embodiments, additionally or alternatively, the selected object may be selected via a selection performed automatically by at least one processor 1304, e.g., using predetermined information, programmed logic, neural networks, machine learning, or other tools such as may relate to artificial intelligence, in some cases. Automatic selection may further be subject to manual confirmation by a user, in some implementations.”)
Lagerling does not explicitly disclose the following, however, in analogous art of marketplace listing and generation, Singh discloses the following:
automatically completing, using the information, the plurality of fields; (Singh: Paragraph 12, “Frequently, purchasers of a product would like to dispose of the product by reselling it before the product has no value or has otherwise diminished in value to such an extent that it is difficult to sell to a new owner. As described above, unfortunately, in many situations, product owners are left with no viable option other than to dispose of a product for little to no value. Embodiments described herein provide systems and methods which allow product owners to identify desirable points in time at which to sell such products for value as well as to reduce the complexity of listing such products for resale. Some embodiments described herein provide systems and methods which allow product owners to specify one or more listing rules that must be met in order for a product to be listed for resale and once met, will cause a product listing to be created substantially automatically.”; Paragraph 26, “In general, user device 140 may be operated by a user who purchases one or more items 110 at a point of sale 120 (e.g., such as “John” in the above illustrative example or the “seller”). User device 140 may be operated to capture or receive information from the point of sale 120 for use in creating an item record in an item database 134 associated with listings platform 130. An illustrative user interface showing a user device 140 and a user interface 242 displaying item data from a point of sale 120 which may be used to create an item record is shown in FIG. 2 User device 140 may also be operated by a user to receive information from the listings platform 130 such as information identifying time(s) when it may be desirable to create an item listing in listings database 136 to offer the item 110 for resale. For example, user device 140 may provide a user interface that presents a user with one or more screens that allows the user to set one or more listing rules that govern when a listing should be published for the item 110. A number of different rules may be created to allow a user to control how and when an item will be listed. For example, a user may set a price target as the main rule that must be satisfied before the item will be listed for sale in a classifieds platform.”; Paragraph 33, “Further details of features of some embodiments will now be described by referring to several flow diagrams that describe processes that may be performed by an application performed by a computing device or a group of computing devices, such as a user device, a web server, a host platform, a cloud computing environment, and the like to implement features of the present invention. Referring to FIG. 5, a process 500 for operating a listings platform to list an item is shown. The process 500 may be performed by platform, such as the listings platform 130 of FIG. 1 interacting with a user device 140 and one or more third party information services 150. In general, the process 500 may begin when (or after) a user purchases an item 110. Process 500 begins at 502 where one or more item data records are created. Item data records may be created in a number of ways. For example, in some embodiments, an item data record may be created substantially automatically in response to a transaction at a point of sale. For example, a point of sale device may be configured to prompt a buyer whether the buyer wishes to create an item data record using information from the point of sale device. The data may be transmitted to listings platform 130 for storage in an item database 134 with information identifying the buyer. As another example, the item data record may be created by a user operating a user device 140. The user device 140 may initiate the creation of the item data record based on, for example, an electronic (or other) receipt received from the point of sale 120 or based on information entered or otherwise captured by the user.”; Paragraph 35, “FIG. 6 depicts a table that represents the item database 134 that may be stored at or accessible to the listings platform 130 according to some embodiments. The table may include, for example, entries identifying items purchased by users of the listings platform 130. The table may also define fields 602, 604, 606, 608, 610 for each of the entries. The fields 602, 604, 606, 608, 610 may, according to some embodiments, specify: an item identifier 602, an associated user identifier 604, an item reference 606, a description 608, and one or more listing rule(s) 610. The item database 600 may be created and updated, for example, based on information received from a buyer and/or that is automatically obtained from a point of sale 120.”; Paragraph 37, “The description 608 includes information describing the item identified by item identifier 602. The description 608 may be entered by a user, or it may be automatically obtained from a source such as a third party information service 150 based on the item reference 606 (for example, a SKU may be used to retrieve a description of the item). Other attributes of the purchase may also be provided in the description 608 (or in other fields of the database). For example, the purchase price, date and location may be provided.”)
Lagerling discloses a method for making suggestions to improve the probability of an item selling. Singh discloses a method for automatically generating a listing for an item according to user defined criteria and rules. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Lagerling with the teachings of Singh in order to improve the value a seller receives for their items while lowering the amount of effort required to do so as disclosed by Singh (Singh: Paragraph 22, “The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort.”)
Claim(s) 16 –
Lagerling in view of Singh discloses the limitations of claim 11
Lagerling further discloses the following:
further comprising refraining from displaying an additional control selectable to automatically generate the listing of the at least one item based on the probability failing to satisfy the threshold value. (Lagerling: Paragraph 68, “The logic flow diagram 600 shows an example of programmatic price reduction, automatically dropping the price of an item listed for sale after a certain amount of time has elapsed since the item was listed for sale (age of the listing, which may correspond to an age of a listed value absent any price drop). Specifically, in this example, when certain conditions are met, e.g., outside of a certain target category (602) or categories of items, and no confident price suggestion exists for a given category, in other words, where there is no a price suggestion satisfying a predetermined confidence condition (604) within a confidence interval for closing the sale within a particular time window, a floor price (FP) may be suggested (606). In this particular example, the first suggestion of a price floor is at 85% of the user-entered listing price, but other default values may be used in other implementations. In some embodiments, the suggested floor price may be presented to a user after the user enters a listing price (LP).”; Paragraph 74, “However, if the user-entered list price is greater than a minimum price of a range based on the predicted value, as determined at 704, then a floor price may be suggested at the minimum price of the range at 706. Responsive to further user input of a floor price, other conditions may be evaluated based on the list price and the floor price.”; Paragraph 77, “As a default option following from 706, if neither condition of 708 or 710 is met (712), then the listing may proceed in similar fashion as with 714 after the floor price is entered. For 720 and 724, these steps may proceed as with 608-612, and the process may repeat for multiples of the average sale time (722).”; Paragraph 79, “Flow diagram 800 represents an example use case in which a user lists a particular item (e.g., iPhone X) for sale (802) on a given platform. The user may be prompted to enter a price or range of prices, e.g., listing price (804) and floor price (806). A suggested price range may be computed and optionally presented to the user. For purposes of the example flow diagram 800, these prices may be entered, computed, and/or presented at t=0 (time at or before creation of an actual listing for sale). Once the listing has been published at t=0, this begins incrementing t with time. For the listed item or corresponding category, brand, category-and-brand pair, etc., of similar items (e.g., Apple smartphones), an average time to sell (avg_sold_time) may be calculated at 24 hours. This calculation may be based on moving-average analysis of historical data on the same platform and/or on other platforms, for example.”)
Claim(s) 17 –
Lagerling in view of Singh discloses the limitations of claim 11
Lagerling further discloses the following:
obtaining one or more performance metrics associated with the listing of the at least one item, wherein the one or more performance metrics comprise one or more of click- through rates, conversion rates, average time spent viewing listings, number of user inquiries, or user engagement volume for one or more additional items associated with the at least one item; (Lagerling: Paragraph 22, “Based at least in part on processing these values, and possibly other platform statistics or performance indicators, a value may be estimated for a prediction or recommendation, which may correspond to a suggested price, in some embodiments. Additionally, certain other values may be generated, such as a minimum and/or maximum value defining a range of values around to the estimated value, thus corresponding to a predicted or recommended range of suggested prices generated from the estimated or predicted value, according to further embodiments.”; Paragraph 27, “Again discussing 118, if execution has flowed to ML-based estimation algorithm(s) 120 for a particular output of object identification module 104, an estimation (prediction, suggestion, etc.) may be performed. To compensate for any potential unreliability in ML-based estimation algorithm(s) 120, a predetermined confidence condition 122 may be checked, to determine whether the predicted value or predicted-value set satisfies the predetermined confidence condition 122. If so, a value may be returned at 124 and end 128 execution of a given function or method for a given output of object identification module 104. If the confidence condition is not satisfied, ML-based estimation algorithm(s) 120 may return NULL at 126. Additionally, or alternatively, a range of values may be generated around the returned value 116. In other embodiments (not shown), a NULL return from 114-116 may result in proceeding to another estimation module, such as a statistics-based estimation module (not shown), for example.”; Paragraph 59, “Moreover, similar to the notifications as described with respect to FIG. 2, if a price has not been automatically lowered after a number of views has peaked (e.g., at a point of “most views” as shown in FIG. 3) and has declined beyond a threshold, a seller may be prompted to lower the price (or to activate a smart-pricing feature such that price is automatically lowered), with the expected benefit of increasing views and potentially increasing likelihood of a sale, according to some embodiments.”; Paragraph 65, “The logic flow diagram 500 shows when, for some embodiments, a promotion factor may be adjusted based on proximity of an existing listing price to a range of prices, such as suggested prices. For example, if a user-provided listing price is in a range that would have been suggested, e.g., for the same or similar items as listed, at which a sale is relatively likely to occur quickly, a listing platform may promote (e.g., increase a likelihood of hits in a search engine or frequency of recommendations of similar products, etc.) the listed item by a given factor, e.g., 3. This may happen if a normalized price is within 10% of a mean value of a range of suggested prices, as shown in FIG. 5. Other degrees of promotion factors (e.g., 2 or 1, etc.) may be provided within different or wider ranges from the mean value. In some use cases, promotion may be a reward for a user who accepts a suggested price from the listing platform, which may further reduce time to sale, likely leading to favorable engagement of the user with the platform.”)
training, based on the one or more performance metrics, a learning model to output the threshold value; and (Lagerling: Paragraph 20, “On the left-hand side of FIG. 1 is a representation of an example subsystem functioning as a table updater 140, which may include or otherwise have access to at least one of a database (DB) 142 and/or a data warehouse 146, from which selected data may be exported or retrieved in batches, e.g., daily export batch 144 and daily export batch 148. While this example provides for daily updates, any periodic interval (e.g., hourly, weekly, etc.) or ad hoc access may be provided, according to some embodiments. Based at least in part on any of the data accessible to table updater 140, such as from a data flow depicted in FIG. 1 at the table updater 140, lists or tables of data may be created, such as automatically created table 130.”; Paragraph 44, “Image-based object recognition may be further configured to detect and interpret barcodes, Quick Response (QR) codes, labels, tags, logos, trademarks, and/or any other defining characteristic of an item. Image-based object recognition may additionally perform optical character recognition (OCR) and interpret text using natural language processing (NLP), in some embodiments. Thus, image-based object recognition may be used to identify listed items, or at least candidate items for confirmation and selection, and may update selected items based on new information and calculations that may be subsequently introduced via various input sources.”; Paragraph 128, “At least one regression model, or equivalent ML model, may be used for extrapolating predicted values or predicted-value sets over time, e.g., based at least in part on historical data, at least one object category, seasonal data, independent secular trends, or any combination thereof, to name a few non-limiting examples. Time-based extrapolations may be updated ad hoc, in response to the determination (automatically determining) that a list of known unique identifiers lacks a given identifier of a selected object, for example.”; Paragraph 129, “In some embodiments, any models, regressions, time-based extrapolations, predicted values, or predicted-value sets may be updated periodically, at least for certain categories or classes of object identifiers that are likely to correspond to input received from users and unlikely to correspond to any entry of a list of known identifiers, e.g., certain types of generic clothing, custom-made craft goods, personalized or one-of-a-kind items, etc. Periodic updates for likely inputs that may lead to ML-based estimation may result in improved speed and accuracy for retrieval of values and/or generation of outputs from performance of certain ML algorithms, for example.”)
obtaining, as output from the learning model, the threshold value. (Lagerling: Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 75, “For example, if the list price and floor price are both above the maximum value of the range based on the predicted value (708), it may be assumed that the user listing the item is not heeding the suggestions provided by the system in the interest of increasing the likelihood of a sale within a reasonable time, and the system may therefore evaluate whether to provide any promotion to the listing, given that the listing may have an unreasonably high price (716).”)
Claim(s) 18 –
Lagerling in view of Singh disclose the limitations of claim 11
Lagerling further discloses the following:
further comprising receiving additional input indicating the threshold value. (Lagerling: Paragraph 73, “For example, at 704, if a user-entered listing price is already at or below the minimum price of a price range generated based on a predicted value from FIG. 1, then the user may not need prompting to reduce the price further at the time of sale. A predetermined floor price may be suggested at 714, similarly to 606 in FIG. 6 as described above. If, for any reason, the item is still not sold after an average length of time has elapsed, smart pricing (automatically lowering price of the item), or a prompt to the user to lower the price automatically or manually, may be automatically provided to the user, according to some embodiments.”; Paragraph 75, “For example, if the list price and floor price are both above the maximum value of the range based on the predicted value (708), it may be assumed that the user listing the item is not heeding the suggestions provided by the system in the interest of increasing the likelihood of a sale within a reasonable time, and the system may therefore evaluate whether to provide any promotion to the listing, given that the listing may have an unreasonably high price (716).”)
Claim(s) 19 –
Lagerling in view of Singh disclose the limitations of claim 11
Lagerling further discloses the following:
wherein the input comprises one or more images of the at least one item. (Lagerling: Paragraph 37, “At the initial input stage, or at any subsequent input stage, system 100 may seek additional user input via the app, for example. Input may be text in the form of a character string, a photographic image, voice recognition, other characteristic sounds or audio fingerprints, for example. Text-based input via the app may prompt a user for a few fields of information depending on a broad category of the item to be listed for sale, rather than an exhaustive description of all features. In many cases, even basic text information may allow the backend to resolve SKU-level data, or at least present a few likely candidate items to a seller for confirmation of the correct item identification.”; Paragraph 40, “In a case of image input, a user attempting to create a new listing for sale may be prompted to take at least one photograph of the item to be listed. Photographic image data from the photograph(s) may be processed locally on the device running the app, or may be transmitted to the backend or to a third party for initial processing and/or further processing. Processing of the photographic image data may include use of artificial intelligence. Processing of the image data may include feeding the image data into a neural network, perceptron, or classifier, for example. The image data may be processed using computer-implemented image recognition, such as using at least one computer vision algorithm, in some embodiments. Any of the above technologies or their equivalents may be used to perform operations such as classification, object recognition and/or reverse image-searching, to name a few non-limiting examples.”)
Claim(s) 20 –
Lagerling in view of Singh disclose the limitations of claim 11
Lagerling further discloses the following:
wherein the context data is based on one or more of an average value associated with the at least one item, an average value associated with one or more items related to the at least one item, a volume of user engagement with the at least one item, or a volume of user engagement with the one or more items related to the at least one item. (Lagerling: Paragraph 69, “After a particular time interval has elapsed (608), at least one of several other actions may be taken. For example, the particular time interval may be an average time between listing and sale (avg_sold_time) for a given item or similar item(s), or for a given category-and-brand pair, etc. The particular time interval may be a scaled factor of average time to sale, for example. Actions to be taken may include dropping the listing price by predetermined gradual steps, e.g., 5% per step (612). Another action that may be taken may include dividing the difference between LP and FP into a predetermined number of steps (610), e.g., three steps, and dropping the price gradually by each step uniformly as a fraction of the difference between LP and FP, according to some embodiments.”; Paragraph 71, “The logic flow diagram 700 shows an example of programmatic price reduction, having some elements in common with logic flow diagram 600 of FIG. 6 (compare 702 to 604, 714 to 606, 720 to 608, and 724 to 610 and 612), but adding additional checks for ranges of values into which various user-entered prices may be found with respect to suggested prices, and accounting for possible user-entered floor price. Additionally, logic flow diagram 700 may provide for additional flexibility on timing, e.g., timing offset(s) (718), for when programmatic price drops may occur, before waiting for the time since listing (age of the listed value, e.g., time since the list price was last reduced) to exceed average time to sale (and repeating in intervals of average time to sale or multiples thereof), for example.”; Paragraph 124, “In 1210, processor 1304 may output at least part of the predicted-value set. The predicted-value set may include or indicate a range of values, for example, at different confidence intervals, or at different statistical frequencies, according to some embodiments. Ranges of values may be defined by orders, series, rankings, representative values, or composite statistical measures such as minimum, maximum, median, quartiles, or quartile ranges (e.g., interquartile range, minimum to first quartile, first quartile to median, median to third quartile, third quartile to maximum, etc.), for example.”)
Claim(s) 21 –
Lagerling in view of Singh disclose the limitations of claim 1
Lagerling further discloses the following:
automatically completing, based on the probability satisfying a second threshold value of the plurality of threshold values and failing to satisfy the first threshold value, a portion of the plurality of fields of the listing of the at least one item, wherein a remaining portion of the plurality of fields are associated with manual completion; and (Lagerling: Paragraph 42, “In further embodiments based on convolutional neural network (CNN) processing, for example, such CNNs may further integrate at least some filters (e.g., edge filters, horizon filters, color filters, etc.). These filters may include, for example, some of the edge detection algorithms, color filtering algorithms, and/or predetermined thresholds. For desired results, a given ANN, including a CNN, may be designed, customized, and/or modified in various ways, for example, according to several criteria, according to some embodiments.”; Paragraph 51, “If a recommended drop exceeds a given threshold, for example, if the price drop is sufficiently near, at, or above the specified maximum price drop, the seller may be notified, e.g., via a push notification, that the price may be too high to sell, that the price should be dropped, that the price will be automatically dropped, and/or at that the price exceeds at least one threshold, any of which may be specified in a notification. Some examples of potential price points for notifications are shown in FIG. 2.”; Paragraph 59, “Moreover, similar to the notifications as described with respect to FIG. 2, if a price has not been automatically lowered after a number of views has peaked (e.g., at a point of “most views” as shown in FIG. 3) and has declined beyond a threshold, a seller may be prompted to lower the price (or to activate a smart-pricing feature such that price is automatically lowered), with the expected benefit of increasing views and potentially increasing likelihood of a sale, according to some embodiments.”)
displaying, based on the probability failing to satisfy a third threshold value of the plurality of threshold values, a configurable template of the listing of the at least one item for the manual completion.(Lagerling: Paragraph 59, “Moreover, similar to the notifications as described with respect to FIG. 2, if a price has not been automatically lowered after a number of views has peaked (e.g., at a point of “most views” as shown in FIG. 3) and has declined beyond a threshold, a seller may be prompted to lower the price (or to activate a smart-pricing feature such that price is automatically lowered), with the expected benefit of increasing views and potentially increasing likelihood of a sale, according to some embodiments.”; Paragraph 87, “Thus, as shown in FIG. 9 within the user interface, a price suggestion may be presented, which may correspond to a certain amount or percentage below an original list price (e.g., 15% below list price), while the user interface may still allow a user to adjust the floor price manually to another price if desired. Other messages may be displayed to the user, such as informational notices of additional costs or fees, or other warnings or suggestions if a manually entered price exceeds a threshold above or below a suggested price, for example.”; Paragraph 88, “In this further example, shown at the right of FIG. 9 is a plot of price over time, according to several graduated price drops over 120 hours in this particular example, assuming no sale within at least the first 96 hours. Price drops may follow patterns or algorithms such as those described with respect to FIGS. 6-8, but other configurations are possible. For example, the times of certain price drops may be at predetermined intervals relative to an initial listing for sale and/or previous price drop. Additionally, or alternatively, timing and/or amount (absolute or relative) of price drops may be determined dynamically in response to activity or demand of other users on at least one online marketplace, such as a platform where the listing is made available for sale.”; Paragraph 126, “In similar context, index, a composite statistic, or composite measure, may refer to any of a mean, median, mode, variance, standard deviation, range, minimum, maximum, quintile, or other ranking from among a set of related values, for example. The output of 1210, however, may be a single composite value, in some cases, or a logical determination of whether a predicted value (or composite value thereof) is equal to, greater than, or less than a predetermined threshold value.”)
Lagerling does not explicitly disclose the following, however, in analogous art of marketplace listing and generation, Singh discloses the following:
automatically completing, based on the probability satisfying a first threshold value of a plurality of threshold values, the plurality of fields of the listing of the at least one item; (Singh: Paragraph 22, “Prior to discussing details of features of the invention, for purposes of illustrating features of some embodiments of the present invention, a simple example will now be introduced and referenced throughout the disclosure. In the illustrative example, a customer (named “John”) is purchasing a new television from a retail store. John knows that he may not keep the television forever and that he may someday want to sell it. At the time of purchase at the retail store, John may interact with the system of the present invention to pre-configure a listing to resell the television using a classifieds platform. Information about the television (including, for example, a SKU or other identifier that can be used to specifically identify the make and model of the television, the purchase price, and the purchase date and location—collectively, the “item data”) are transmitted to a listings platform for storage in a record associated with an account John has with the listings platform. John can also pre-configure one or more listing rules about how or when he will list the television for resale in the future. For example, John may specify that he wants to list the television for resale after 1 year if there is a high probability that he can resell the television for at least 50% of the original purchase price. John then takes his new television home and enjoys it. Over a year later, John receives a notification from the listings platform (based on the rules that John established earlier) that there is strong demand for his make and model of television and that he will likely be able to resell the television for over 50% of the original purchase price. John responds to the notification confirming that he wishes the listings platform to proceed with a listing to attempt to sell the television. In response, the listings platform automatically posts a listing for the television including details of the television (such as the television's make, model, age and offer price) without any further intervention from John. The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort. Users do not need to be experts in pricing or marketing, as embodiments analyze market conditions to identify appropriate timing and pricing to increase the likelihood of a product being sold.”; Paragraph 24, “For example, pursuant to some embodiments, the listings platform 130 may be configured to allow users to create item data records (stored in item database 134) and to specify one or more listing rules 132 which, if met, cause the item data to be converted into listing data (e.g., such as that stored in listing database 136). The listing rules 132 may be established by users associated with the item data, and the listings platform 130 may be operated to regularly monitor the listing rules 132 to identify situations where the listing rules for a particular item have been met so that the item data can be converted into a listing. Pursuant to some embodiments, listing rules 132 may involve information associated with one or more third party information services 150. For example, in some embodiments, pricing data from third party listing services may be monitored to identify item prices for similar items. In some embodiments, listing rules 132 may involve information associated with an entity operating the system of the present invention. For example, in some embodiments, an entity operating a system of the present invention may gather and analyze information associated with price decay of categories of items sold through a system operated by or on behalf of the entity. The price decay data or models may be used in association with listing rules 132 as described further herein. Pursuant to some embodiments, listing rules 132 may involve information available to the listings platform 130 such as, for example, current pricing of similar items, current demand, inventory of similar products, information associated with the speed at which similar items are sold, or the like. In some embodiments, a metric referred to as the “ETA of Sale” may be calculated to determine that conditions are suitable for a listing of the item. Further details of the ETA of Sale and listing rules will be provided further below.”; Paragraph 26, “In general, user device 140 may be operated by a user who purchases one or more items 110 at a point of sale 120 (e.g., such as “John” in the above illustrative example or the “seller”). User device 140 may be operated to capture or receive information from the point of sale 120 for use in creating an item record in an item database 134 associated with listings platform 130. An illustrative user interface showing a user device 140 and a user interface 242 displaying item data from a point of sale 120 which may be used to create an item record is shown in FIG. 2 User device 140 may also be operated by a user to receive information from the listings platform 130 such as information identifying time(s) when it may be desirable to create an item listing in listings database 136 to offer the item 110 for resale. For example, user device 140 may provide a user interface that presents a user with one or more screens that allows the user to set one or more listing rules that govern when a listing should be published for the item 110. A number of different rules may be created to allow a user to control how and when an item will be listed. For example, a user may set a price target as the main rule that must be satisfied before the item will be listed for sale in a classifieds platform.”)
Lagerling discloses a method for making suggestions to improve the probability of an item selling. Singh discloses a method for automatically generating a listing for an item according to user defined criteria and rules. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Lagerling with the teachings of Singh in order to improve the value a seller receives for their items while lowering the amount of effort required to do so as disclosed by Singh (Singh: Paragraph 22, “The result is a system that allows users to maximize the resale value of their purchases with a minimum of user-initiated effort.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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