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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 10: Claim 10 recites a system for publishing products on multiple platforms, wherein the system is configured to execute an operation instruction included in the method for publishing products on multiple platforms of claim 1.
While the claim recites a system, the claim is not clear whether the system of the claim is a physical system or a system of a series of steps. Additionally, the claims do not recite any elements of the system except that it performs an operation instruction included in the method of claim 1, and as such, it is also not clear whether the claim performs only a single operation of the various limitations of claim 1 and which operation that would be, or if it performs the entire method of claim 1.
As such, claim 10 is indefinite and does not clearly define the metes and bounds of the claim as it is unclear what exactly the system is, and what exactly it performs. For the sake of compact prosecution, the system of claim 10 will be interpreted as a physical system.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-9 are directed to a method, which is a process. Claim 10 is directed to a system, which is an apparatus, but does not disclose any physical structure of elements of the system (see 112(b) rejection above). Therefore, claims 1-9 are directed to one of the four statutory categories of invention, and claim 10 is not directed to one of the four statutory categories of invention. For the sake of compact prosecution, claim 10 will be addressed in this rejection as a proper system claim, but appropriate correction is required.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of publishing a listing to multiple platforms based on listing requirements:
determining, in response to a platform determination instruction, ports of platforms corresponding to the platform determination instruction;
obtaining initial product data in response to a first publishing instruction, and generating first publishing information based on the initial product data, wherein the first publishing information comprises a publishing store, a product inventory, a product weight, and a logistics mode, and the first publishing information is mandatory information to be filled in when publishing products on each of the platforms;
identifying the initial product data to generate second publishing information, wherein the second publishing information comprises product category information, and output a plurality of classification prediction values corresponding to the product attribute categories, and the product category information is generated based on the classification prediction values;
detecting a blank mandatory item related to product attributes in a publishing page of each of the platforms, and setting a preset sequence option of the blank mandatory item as third publishing information;
sending the first publication information, the second publishing information, and the third publishing information to the ports of the platform to publish a product corresponding to the initial product data on each of the platforms.
The recited limitations above set forth the process for publishing a listing to multiple platforms based on listing requirements. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to obtaining product data, and generating publishing information by determining classification prediction values, a blank mandatory item, and sending the publishing information to multiple platforms (see specification [0005] disclosing the problem of matching publishing data when publishing to multiples platforms as opposed to within the same platform), which is an advertising and marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
e-commerce;
based on a preset model;
the preset model comprises a plurality of fully connected layers corresponding to product attribute categories of the platforms;
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite e-commerce, this only defines a particular environment for the sales activity to take place in. The claims do not recite any particular improvements or changes in computers or any other technical field, but merely implements sales activities on a computing device over a network. The preset model and layers are also recited with a very high level of generality, the claims merely reciting that they are configured to output values of the abstract idea (a plurality of classification prediction values). Specification paragraph [0118] discloses the model as a deep model, but does not provide any details regarding the underlying technology of the model, except that it is trained with a single type of data, which only represents the abstract data that is utilized. There are no specific details disclosed in the specification regarding how the model functions at a technical level. It is merely applied to the abstract idea to output values of the abstract idea.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 10 (system): Claim 10 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 10 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-9 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of publishing a listing to multiple platforms based on listing requirements, and do not recite any further additional elements. Thus, each of claims 2-9 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-9 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-9 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged (e.g., training a preset model (claim 2); querying a database (claim 5) a pop-up interactive interface (claim 6)); however, the additional elements of claims 2-9 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-9 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-9 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-9 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable by Sotelo (US 20170169014 A1) in view of Viswanathan (US 10,438,264 B1).
Regarding Claim 1: Sotelo discloses a method comprising:
determining, in response to a platform determination instruction, ports of e-commerce platforms corresponding to the platform determination instruction; (Sotelo: [0055] – “the graphical user interface contains additional controls for identifying one or more second listing websites. The graphical user interface may include a drop down menu for identifying a particular website or geographic region and a control for adding the particular website or geographic region to a list of locations to which the listing is propagated”).
obtaining initial product data in response to a first publishing instruction, and generating first publishing information based on the initial product data, wherein the first publishing information comprises a publishing store, a product inventory, a product weight, and a logistics mode, and the first publishing information is mandatory information to be filled in when publishing products on each of the e-commerce platforms; (Sotelo: [0058] – “the one or more first listings and additional listing data of the one or more listings are received. Translation server computer 100 may store the received listings in listing data repository. In an embodiment, translation server computer 100 separates the listings into field names and field values. For example, a listing may be broken up into the title, the description, the price, the available quantity, and the category of the item. Some listings may also include additional fields, such as color and size, while such fields are absent in other listings. Translation server computer 100 may identify each field in the listing and store data describing the field and the field value”; Sotelo: [0099] – “Translation server computer 100 may execute listing generation instructions 112 to identify first region specific values in the first listing and convert the first region specific values into second region specific values for the second listing. A region specific value may include currency values. Translation server computer 100 may periodically receive exchange rates for various currency types over a network. Additionally and/or alternatively, translation server computer 100 may request exchange rates over a network based on received listings”).
identifying the initial product data based on a preset model to generate second publishing information, wherein the second publishing information comprises product category information; (Sotelo: [0062] – “one or more first listings are translated from a first language to one or more second languages. For example, translation server computer 100 may identify one or more second listing websites in which to propagate the one or more first listings. For each of the one or more second listing websites, translation server computer 100 may identify a language associated with the listing website. For example, a listing website that is displayed in Germany may be in German while a listing website displayed in Israel may be in Hebrew. Translation server computer 100 may execute translation instructions 110 to translate the one or more first listings from the first language to the one or more second languages corresponding to the one or more second listing websites”).
detecting a blank mandatory item related to product attributes in a publishing page of each of the e-commerce platforms, and setting a preset sequence option of the blank mandatory item as third publishing information; (Sotelo: [0114] – “translation server computer 100 may augment a listing with additional stored data regarding a particular inventory item… if a listing website server requires a screen size to be input into a screen size field, translation server computer 100 may use the screen size from the stored specifications for the specific brand of computer monitor to complete the required field”). In summary, an undefined field can be filled with preset information from the stored specifications.
sending the first publication information, the second publishing information, and the third publishing information to the ports of the e-commerce platforms to publish a product corresponding to the initial product data on each of the e-commerce platforms. (Sotelo: [0062] – “one or more first listings are translated from a first language to one or more second languages. For example, translation server computer 100 may identify one or more second listing websites in which to propagate the one or more first listings. For each of the one or more second listing websites, translation server computer 100 may identify a language associated with the listing website. For example, a listing website that is displayed in Germany may be in German while a listing website displayed in Israel may be in Hebrew. Translation server computer 100 may execute translation instructions 110 to translate the one or more first listings from the first language to the one or more second languages corresponding to the one or more second listing websites”).
Sotelo does not explicitly teach a method comprising the preset model comprises a plurality of fully connected layers corresponding to product attribute categories of the e-commerce platforms, the plurality of fully connected layers are respectively configured to output a plurality of classification prediction values corresponding to the product attribute categories, and the product category information is generated based on the classification prediction values; Notably, however, Sotelo does disclose utilizing machine learning to determine rules for post (Sotelo: [0078]).
To that accord, Viswanathan does teach a method comprising the preset model comprises a plurality of fully connected layers corresponding to product attribute categories of the e-commerce platforms, the plurality of fully connected layers are respectively configured to output a plurality of classification prediction values corresponding to the product attribute categories, and the product category information is generated based on the classification prediction values; (Viswanathan: col. 14, ln. 27-43 – “the confidence score may represent the likelihood of the candidate value being correct for the feature in question given the item provided by the user and the item category. The process 500 may include transmitting, to a user interface of a user device, the image of the item and a particular candidate value based on the confidence score of the particular candidate value for a feature at 510. In some embodiments, the user interface may include one or more inter-actable selections, menus, drop downs, or buttons for selecting the particular candidate value from a portion or subset of candidate values that are selected and presented to the user based on their respective confidence scores. In various embodiments, the portion or subset of candidate values selected for presentation to the user via the user interface may be ranked and a certain number of the ranked candidate values may be provided in the user interface based on a threshold”; Viswanathan: col. 15, ln. 51-54 – “a machine learning algorithm using a model may generate and assign the confidence scores to each potential candidate value and then rank the candidate value to confidence score pairs for each feature”; Viswanathan: col. 16, ln. 28-32 – “It should be understood that there can be several application servers, layers, or other elements, processes, or components, which may be chained or otherwise configured, which can interact to perform tasks”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Sotelo disclosing the system for publishing a listing to multiple platforms based on their requirements with the machine learning layers outputting classification prediction values to determine product category information as taught by Viswanathan. One of ordinary skill in the art would be motivated to do so in order to make a less frustrating experience to enter correct information into the field of the listing (Viswanathan: col. 1, ln. 23-42).
Regarding Claim 5: Sotelo in view of Viswanathan discloses the limitations of claim 1 above.
Sotelo further discloses a method comprising:
inputting the initial product data into the preset model; (Sotelo: [0058] – “the one or more first listings and additional listing data of the one or more listings are received. Translation server computer 100 may store the received listings in listing data repository. In an embodiment, translation server computer 100 separates the listings into field names and field values. For example, a listing may be broken up into the title, the description, the price, the available quantity, and the category of the item. Some listings may also include additional fields, such as color and size, while such fields are absent in other listings. Translation server computer 100 may identify each field in the listing and store data describing the field and the field value”).
generating the second publishing information based on the classification information and the target classification prediction value. (Sotelo: [0062] – “identify one or more second listing websites in which to propagate the one or more first listings. For each of the one or more second listing websites, translation server computer 100 may identify a language associated with the listing website. For example, a listing website that is displayed in Germany may be in German while a listing website displayed in Israel may be in Hebrew. Translation server computer 100 may execute translation instructions 110 to translate the one or more first listings from the first language to the one or more second languages corresponding to the one or more second listing websites”).
Sotelo does not explicitly teach a method comprising:
identifying the initial product data based on the preset model to output a target classification prediction value corresponding to a minimum product attribute category;
querying a database based on the target classification prediction value to determine classification information respectively corresponding to each level of product attribute categories;
Notably, however, Sotelo does disclose utilizing machine learning to determine rules for post (Sotelo: [0078]).
To that accord, Viswanathan does teach a method comprising:
identifying the initial product data based on the preset model to output a target classification prediction value corresponding to a minimum product attribute category; (Viswanathan: col. 14, ln. 27-43 – “the confidence score may represent the likelihood of the candidate value being correct for the feature in question given the item provided by the user and the item category. The process 500 may include transmitting, to a user interface of a user device, the image of the item and a particular candidate value based on the confidence score of the particular candidate value for a feature at 510. In some embodiments, the user interface may include one or more inter-actable selections, menus, drop downs, or buttons for selecting the particular candidate value from a portion or subset of candidate values that are selected and presented to the user based on their respective confidence scores. In various embodiments, the portion or subset of candidate values selected for presentation to the user via the user interface may be ranked and a certain number of the ranked candidate values may be provided in the user interface based on a threshold”; Viswanathan: col. 15, ln. 51-54 – “a machine learning algorithm using a model may generate and assign the confidence scores to each potential candidate value and then rank the candidate value to confidence score pairs for each feature”; Viswanathan: col. 16, ln. 28-32 – “It should be understood that there can be several application servers, layers, or other elements, processes, or components, which may be chained or otherwise configured, which can interact to perform tasks”).
querying a database based on the target classification prediction value to determine classification information respectively corresponding to each level of product attribute categories; (Viswanathan: col. 15, ln. 37-58 – “generating one or more candidate values for a feature of the item based in part on the information at 606. As described herein, one or more key features may be identified for the item based on an item category associated with the item and the one or more candidate values may be extracted from unstructured/structured text provided by the user with the image, similar items in an item catalog, manufacturers, reviews of the item, or question and answer features related to a similar item that are maintained by an electronic marketplace. The process 600 may conclude at 608 by transmitting to the second computer system the image of the item and a ranked portion of candidate values for the feature where the candidate values are ranked according to associated confidence scores. In embodiments, a machine learning algorithm using a model may generate and assign the confidence scores to each potential candidate value and then rank the candidate value to confidence score pairs for each feature. The process may include generating a web page to offer the item identified in the image using a particular candidate value of the ranked portion of candidate values for the feature that was selected based on input from a user”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Sotelo disclosing the system for publishing a listing to multiple platforms based on their requirements with the identifying initial product data and querying a database based on the classification prediction value as taught by Viswanathan. One of ordinary skill in the art would be motivated to do so in order to make a less frustrating experience to enter correct information into the field of the listing (Viswanathan: col. 1, ln. 23-42).
Regarding Claim 8: Sotelo in view of Viswanathan discloses the limitations of claim 1 above.
Sotelo further discloses a method comprising:
determining a target claim rule corresponding to the first publishing instruction in a database in response to the first publishing instruction, wherein a plurality of claim rules are stored in the database, and the plurality of claim rules correspond to different initial product data respectively; (Sotelo: [0040] – “authorizing translation server computer 100 to access the listings, identifying one or more other listing websites, and establishing rules for listing translation, generation, and/or selection.”).
obtaining the initial product data corresponding to the target claim rule. (Sotelo: [0046] – “generate one or more listings from the translations created by executing translation instructions 110. Generating the one or more listings may comprise identifying fields in the translated listings, identifying corresponding available fields used by second listing website server 140, and inputting the translated data from the translated listings into the corresponding available fields. Generating the one or more listings may comprise identifying fields in the translated listings that do not have corresponding available fields in the second listing website, extracting data from the fields in the translated listings, and placing the data in a different field used by second listing website server 140. Generating the one or more listings may also comprise identifying fields used by the second listing website server that do not correspond to fields from the translated listings, identifying data in the translated listings that correspond to the identified fields, and inputting the identified data into the identified fields”).
Regarding Claim 10: Claim 10 recites substantially similar limitations as claim 1. Therefore, claims 10 is rejected under the same rationale as claim 1 above.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Sotelo (US 20170169014 A1) and Viswanathan (US 10,438,264 B1), in view of Saad (US 20230350963 A1).
Regarding Claim 2: The combination of Sotelo and Viswanathan discloses the limitations of claim 1 above.
Sotelo further discloses obtaining historical publishing data corresponding to products of each of the e-commerce platforms; (Sotelo: [0119] – “Additional listing data 144 may include past transaction data for any other listing websites. For example, translation server computer 100 may perform the methods described herein for customers with listings in a wide variety of locations. For each customer, translation server computer 100 may receive listings and additional listing data which includes past transaction information. Translation server computer 100 may identify, through the past transaction information, locations in which a particular inventory item is in high demand”).
The combination does not explicitly teach training a preset model corresponding to each of the e-commerce platforms based on the historical publishing data. Notably, however, Sotelo does disclose utilizing machine learning to determine rules for post (Sotelo: [0078]).
To that accord, Saad does teach training a preset model corresponding to each of the e-commerce platforms based on the historical publishing data. (Saad: [0036] – “The item interactions module 110 uses item data 114 and the historical listing data 116 to train a machine learning model to model the pairwise interaction between items and their correlations with the propensity to cause a user to purchase or otherwise interact with an item on a listing platform. Training the machine learning model begins with using the item data 114 and the historical listing data 116 to search for pairwise interactions between items on a listing platform.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Sotelo and Viswanathan disclosing the system for publishing a listing to multiple platforms based on their requirements with the training the model based on the historical publishing data as taught by Saad. One of ordinary skill in the art would be motivated to do so in order to identify interactions between users and listings on one or more listing platforms (Saad: [0002]).
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Sotelo (US 20170169014 A1) and Viswanathan (US 10,438,264 B1), in view of Wu (US 11,145,017 B1).
Regarding Claim 6: The combination of Sotelo and Viswanathan discloses the limitations of claim 1 above.
Sotelo further disclose generating the first publishing information, the second publishing information and the third publishing information based on the product publishing data. (Sotelo: [0062] – “identify one or more second listing websites in which to propagate the one or more first listings. For each of the one or more second listing websites, translation server computer 100 may identify a language associated with the listing website. For example, a listing website that is displayed in Germany may be in German while a listing website displayed in Israel may be in Hebrew. Translation server computer 100 may execute translation instructions 110 to translate the one or more first listings from the first language to the one or more second languages corresponding to the one or more second listing websites”).
The combination does not explicitly teach a method comprising:
in response to a second publishing instruction, generating a popup interactive interface based on mandatory items of a publishing page of the e-commerce platform;
obtaining the first publishing data based on the popup interactive interface;
Notably, however, Sotelo does disclose where the second websites have fields that do not correspond to the first information (Sotelo: [0046]).
To that accord, Wu does teach a method comprising:
in response to a second publishing instruction, generating a popup interactive interface based on mandatory items of a publishing page of the e-commerce platform; (Wu: col. 26, ln. 23-26 – “A notification can be provided to the user to identify any blank fields that should have a signature or date. The notification can be provided by a pop-up notification or by highlighting the blank field”).
obtaining the first publishing data based on the popup interactive interface; (Wu: col. 26, ln. 23-28 – “A notification can be provided to the user to identify any blank fields that should have a signature or date. The notification can be provided by a pop-up notification or by highlighting the blank field”). In summary, while Wu does not teach the listing data, Sotelo and Wu in combination teach filling listing information, and a pop-up notification for required information in empty fields.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Sotelo and Viswanathan disclosing the system for publishing a listing to multiple platforms based on their requirements with the popup interface for mandatory items and receiving the mandatory items as taught by Wu. One of ordinary skill in the art would have been motivated to do so in order to check that everything is populated and no errors exist (Wu: col. 26, ln. 26-28).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Sotelo (US 20170169014 A1), Viswanathan (US 10,438,264 B1), and Wu (US 11,145,017 B1), in view of Li (US 20100228711 A1).
Regarding Claim 7: The combination of Sotelo, Viswanathan, and Wu discloses the limitations of claim 6 above.
The combination of Sotelo and Viswanathan does not explicitly teach a method comprising:
in response to detecting that a mandatory item corresponding to the first publishing information is blank, marking the mandatory item based on a preset display special effect;
obtaining the product publishing data corresponding to the mandatory item based on the popup interactive interface, wherein the product publishing data is higher than the initial product data in data usage priority.
Notably, however, Sotelo does disclose where the second websites have fields that do not correspond to the first information (Sotelo: [0046]).
To that accord, Wu does teach a method comprising:
in response to detecting that a mandatory item corresponding to the first publishing information is blank, marking the mandatory item based on a preset display special effect; (Wu: col. 15, ln. 20-27 – “Each defined form element 144 is associated with a data field identifier that indicates a type of data to populate that data field. The defined form elements can be highlighted, color coded, or otherwise marked to indicate a blank field that must be populated by the user”).
obtaining the product publishing data corresponding to the mandatory item based on the popup interactive interface; (Wu: col. 26, ln. 23-26 – “A notification can be provided to the user to identify any blank fields that should have a signature or date. The notification can be provided by a pop-up notification or by highlighting the blank field”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Sotelo and Viswanathan disclosing the system for publishing a listing to multiple platforms based on their requirements with the visual effect for mandatory items and a popup interface for mandatory items and receiving the mandatory items as taught by Wu. One of ordinary skill in the art would have been motivated to do so in order to check that everything is populated and no errors exist (Wu: col. 26, ln. 26-28).
The combination does not explicitly teach wherein the product publishing data is higher than the initial product data in data usage priority. Notably, however, Sotelo does disclose where the second websites have fields that do not correspond to the first information (Sotelo: [0046]).
To that accord, Li does teach wherein the product publishing data is higher than the initial product data in data usage priority. (Li: [0086] – “determines whether the expertise resource data was system extracted or by user input.(user input takes the higher priority)”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Sotelo, Viswanathan, and Wu disclosing the system for publishing a listing to multiple platforms based on their requirements with the publishing fata is higher priority than initial product data as taught by Li. One of ordinary skill in the art would have been motivated to do so in order to provide better results with better defined data from authors (Li: [0002]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Sotelo (US 20170169014 A1) and Viswanathan (US 10,438,264 B1), in view of Fan (US 20250005654 A1).
Regarding Claim 9: The combination of Sotelo and Viswanathan discloses the limitations of claim 1 above.
Sotelo further discloses a method comprising:
determining a number of stores, comparing the number of stores with a preset number of stores; (Sotelo: [0118] – “identify locations from which a computing device has viewed the inventory item, purchased the inventory item, or to which the inventory item has been sent. In response to identifying a demand from a geographic region other than the region of first listing website server 130, translation server computer may select the geographic region as a location to which to propagate the one or more first listings”). In summary, the amount of demand is compared with the region to determine whether to propagate the listings.
in response to that the number of stores is greater than or equal to the preset number of stores; (Sotelo: [0118] – “server computer may select the geographic region as a location to which to propagate the one or more first listings”).
The combination does not explicitly teach a method comprising:
determining a product inventory based on the first publishing information, and comparing the product inventory with a preset inventory;
in response to that the product inventory is greater than or equal to the preset inventory, obtaining a target product traffic word with a search ranking less than or equal to a preset ranking on the e-commerce platforms according to the product category information;
setting a product name of the first publishing information based on the target product traffic word.
Notably, however, Sotelo does disclose using an n-gram repository to determine the information for filling in the listing information (Sotelo: [0066]), and determining a title for the listing based on n-grams (Sotelo: [0068]), and determining an available quantity of the item (Sotelo: [0039]).
To that accord, Fan does teach a method comprising:
determining a number of stores and a product inventory based on the first publishing information, comparing the number of stores with a preset number of stores, and comparing the product inventory with a preset inventory; (Fan: [0105] – “predicted availability 425 of item 409 (“jalapeno peppers”) is less than the threshold predicted availability, while predicted availability 423, predicted availability 427, and predicted availability 429 are greater than the threshold predicted availability”).
in response to that the number of stores is greater than or equal to the preset number of stores and the product inventory is greater than or equal to the preset inventory, obtaining a target product traffic word with a search ranking less than or equal to a preset ranking on the e-commerce platforms according to the product category information; (Fan: [0106] – “To account for predicted availability 425 of item 409 being less than the threshold predicted availability, the online concierge system 140 applies a position modification model 440 to item attributes 430 of item 409 and to search query attributes 435 of search query 400 in the example of FIG. 4. In various embodiments, the position modification model 440 also receives one or more customer characteristics of the customer from whom the search query 400 was received. As further described above in conjunction with FIG. 3, the position modification model 440 is trained to determine the position modification 445 for item 409, which identifies a reduction in a position of item 409 in a ranking of the set of items 405. The position modification model 440 determines a position modification 445 for item 409 in the ranking 410 based on one or more of: the search query attributes 435 of search query 400, the item attributes 435 of item 409, and the customer characteristics of the customer from whom search query 400 was received. The position modification 445 specifies an amount by which the position of item 409 in the ranking is reduced or specifies a modified position in the ranking 410 of item 409 that is less than the current position of item 409 in the ranking”). In summary, when the availability is compared to a threshold, the item is ranked higher or lower based on their search query attributes (keywords).
setting a product name of the first publishing information based on the target product traffic word. (Fan: [0106] – “determines a position modification 445 for item 409 in the ranking 410 based on one or more of: the search query attributes 435 of search query 400, the item attributes 435 of item 409, and the customer characteristics of the customer from whom search query 400 was received”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Sotelo, Viswanathan, and Wu disclosing the system for publishing a listing to multiple platforms based on their requirements with the determining a product inventory compared to a preset inventory, obtaining a target product traffic word with a search ranking, and setting a product name as taught by Fan. One of ordinary skill in the art would have been motivated to do so in order to reduce a likelihood of an item being included in an order that is likely to be unavailable (Fan: [0106]).
Subject Matter Free of Prior Art
Claims 3-4 are determined to have overcome the prior art of rejection and are free of the prior art, however, the claims remain rejected under 35 U.S.C. 101, as set forth above.
Claims 3-4 are found to overcome the prior art rejection for the reasons as set forth below.
Claim 3 recites the claimed features of:
determining a data source identifier corresponding to the historical publishing data;
classifying the historical publishing data into first publishing data and second publishing data according to the data source identifier, wherein the first publishing data is historical publishing data that is not modified by a user, and the second publishing data is historical publishing data that is modified by a user;
training a preset model corresponding to each of the e-commerce platforms based on the first publishing data;
verifying accuracy of the preset model in outputting the classification prediction values based on the second publishing data.
Claim 4 is free of the prior art rejections as it is dependent upon claim 3.
The closest prior art was found to be as follows:
Wolfe (US 20210374619 A1) discloses [0036] – “the debt resolution planning platform may obtain the user inputs, account data, predictive charge off score, and/or historical data stored in a data structure, and portion the data into a training set, a validation set, a test set, and/or the like. In some implementations, the debt resolution planning platform may train the model to determine plan parameters based on the user inputs, account data, predictive charge off score, and/or historical data using, for example, an unsupervised training procedure based on the training set of data”).
While Wolfe discloses portioning historical data into a training set and a validation set, there is no disclosure of the training set being unmodified, and the validation set being modified. Furthermore, the historical publishing data is not classified into a first publishing data and second publishing data according to the data source identifier.
Zhu (US 202300884466 A1) discloses [0133] – “the computer device may acquire posterior consumption data of historical multimedia resources from the databases, and may select positive and negative samples based on the posterior consumption data to build a training set and a verification set. The positive sample may indicate that the quality label corresponding to the historical multimedia resource on a certain task is a positive label, while the negative sample may indicate that the quality label corresponding to the historical multimedia resource on a certain task is a negative label. The computer device may select one part of the historical multimedia resources as the training set, and may train the multimedia resource classification model based on the training set to obtain the trained multimedia resource classification model. The computer device may acquire the other part of the historical multimedia resources as the verification set, may verify the trained multimedia resource classification model based on the verification set, and may calculate classification accuracy of the trained multimedia resource classification model based on the verification set.”.
While Zhu discloses generating a training set and a verification set from historical data, there is no disclosure of the training set being unmodified, and the validation set being modified. Additionally, while Zhu is directed to resource classification model, it is in regard to multimedia resources, and are different from classifying classification prediction values of product listings. Zhu also fails to disclose the historical publishing data being classified into a first publishing data and second publishing data according to the data source identifier.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
determining a data source identifier corresponding to the historical publishing data;
classifying the historical publishing data into first publishing data and second publishing data according to the data source identifier, wherein the first publishing data is historical publishing data that is not modified by a user, and the second publishing data is historical publishing data that is modified by a user;
training a preset model corresponding to each of the e-commerce platforms based on the first publishing data;
verifying accuracy of the preset model in outputting the classification prediction values based on the second publishing data.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 3-4 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hudak (US 20080222010 A1) discloses claim 1 – “a selection of which one or more recommended online listing platforms to use to attempt to sell the article and a sale price of the article for each; creating, for each selected online listing platform, a listing to offer the article for sale, each listing including a description of the article, the sale price, and at least one mechanism to track the sale process each of the online listing platforms, each listing being formatted in a manner that conforms to the requirements of that particular listing platform”.
Dang (US 11,270,370 B1) discloses col. 5, ln. 26-31 – “may receive information from user 120 through template 118, and upload or generate sales listings 122 or the vehicle 124 across multiple different websites 126, adjusting the format 130 accordingly, based on the data or listings 104A-C particular for each website 126 or across different websites”.
PTO-892 Reference U discloses web design of product listing pages, and their influence on traffic and sales volume on a website, such as the presentation and information format, and how the arrangement and display type of the information results in better recall of brand names and product images, and more positive attitudes towards using the website.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 8:30am - 7:00pm EST.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/18/2026
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