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 .
Response to Remark(s)
Applicant's amendment filed January 21st, 2026 have been fully entered and considered. Applicant’s amendment to the claims have overcome each and every claim objection previously set forth in the Non-Final Office Action mailed on October 21st, 2025. Regarding the arguments to the previous prior art rejections and the 101 rejections, the examiner respectfully finds the arguments to be non-persuasive, see response to remarks section below. Accordingly, this action is made final.
Status of Claims
Claims 1-20 are pending, claims 1-2, 6-7, 10-11 and 16-18 have been amended. Claims 1-20 remains rejected.
Response to Argument(s)
101 rejections:
In pages 8-10 of the Applicants’ remarks, the Applicants argue that the amended independent claims 1, 10 and 16 integrate the alleged abstract idea into a practical application by identifying trends of user demand that the online platform has not based on aggregated query image feature data for a plurality of query images, such as supported in the instant specification’s [0010] and [0014] which described the improvement provided by the claimed invention to the technologies of demand detection, or more specifically detection of user demand for an item based on query images, hence provides an improve in the functioning of a computer, or an improvement to other technology or technical field.
Examiner’s reply:
The examiner respectfully disagrees with the Applicants’ arguments and find them to be incommensurate with the scope of the claims. Moreover, the Applicants are reminded that the claims are construed based on BRI (broadest reasonable interpretation) in light of the specification, therefore, the direct teachings form the instant specification cannot be imported to be the scope of the claims, the claims’ scopes is based solely on its language. Therefore, the examiner finds the claims to not reflect the Applicants’ argument and that is not reflected the mentioned paragraphs from the specification, Specifically, the claims do not recite identifying trends of user demand that the online platform ahs not based on aggregated query image feature for a plurality of query images, this argument only involves negative limitations which are clearly not recited in the claim based on language and its scope.
Neither, the claims provide any improvement to functionality of a computer functioning, the claim still merely uses generic computer with a processor executing instructions of the invention. There is no improvement to technology of demand detection, the closet limitation can be found at the limitation of “generating, by a demand analysis model, demand data indicating a level of user demand for an object, based on comparing the query theme to catalog data of an online platform” which is a mere recitation of a general mental process abstract idea implemented by a generic module, wherein the human mind can perform comparing data/information, here being comparing the query theme (already given observable information) to catalog data of an online platform (already given observable information) to generate a demand data indicating a level of user demand, without further limiting what is a specific application of this generation step, and what is the generated demand data is used for, but merely attempt to generate it without a practical application, moreover, it’s not specific and broad that a demand data indicating a level of user demand, therefore, any level can be included, there is no specific level or output being identified by a broad and general term “level,” which by BRI can be anything that indicating a use/querying/searching/demand for anything in the image, therefore, the examiner finds the claims to not reflect any integration of the judicial exceptions into a practical application.
The amended claims remain rejected under 101.
Prior art 102 and 103 rejections:
In pages 10-12 of the remarks, the Applicants argue that the proposed prior arts, alone of in combination, does not teach or suggest the features of the amended claims, specifically the independent claims, such as for claim 1:
“identifying, by a theme identification module, a query theme based on aggregated query image feature data;
generating, by a demand analysis module, demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform;”
Regarding the limitation of “identifying, by a theme identification module, a query theme based on aggregated query image feature data,” in support of the above argument, the Applicants assert that the proposed prior arts, such as Ghadekar (previous used for the 102 rejection of the independent claims 1, 10 and 16) was alleged to teach a transaction list in equation 1 can be understood be a query theme, however, the Applicants find the transaction list or the items included in one transaction or multiple transactions are not equivalent to a query theme; moreover, a transaction involves buying or selling one or more items and that is the items in equation 1 are items actually bought, not queried. In contrast, the query theme in claim 1 at issue refers to one or more object categories reflected in a collection of query images, and determined based on aggregated query image feature data, as supported by the instant specification’s [0020].
Regarding the limitation of “generating, by a demand analysis module, demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform,” in support of the above argument, the Applicants assert that the proposed prior arts, such as Ghadekar (previous used for the 102 rejection of the independent claims 1, 10 and 16) does not teach or suggest this limitation, importantly, the Applicants finds the instant specification’s [0020] to indicate that “demand data indicating a level of user demand for an object,” while Ghadekar has its user interest is based on whether a user clicks an image of the product or puts the product in a wish-list. In contrast, the claimed invention has its demand data at issue is based on comparing a query theme to a catalog data of an online platform. Moreover, Ghadekar’s user interest or produce recommendation is focused on individual users, but claim 1’s demand data is focused on certain produces or items that can be on demand from the perspective of a plurality of users.
Examiner’s reply:
The examiner respectfully disagrees with the Applicants’ arguments, and find them to be incommensurate with the scope of the claims.
Moreover, regarding the limitation of “identifying, by a theme identification module, a query theme based on aggregated query image feature data,” the Applicants are respectfully reminded that the claims are construed based on BRI in light of the specification, therefore the bringing in of the specification’s to be the instant of scope of the claim is not persuasive, such as the Applicants alleging that the instant specification’s [0020] discloses that a “query theme refers to one or more object categories reflected in a collection of query images, and determined based on aggregated query image feature data” is not fully reflected in the claim, nowhere in the claim provide a specific definition to what a query theme is, neither to say that the claim recites any definition that a query theme refers to one or more object categories reflected in a collection of query images, the claim does recite “a query theme determined based on aggregated query image feature data,” however, the query theme is merely based on such step which is broad and covering that the query theme is related to, based on, is part of such process, not a definition of a query theme. The examiner finds the proposed Ghadekar to teach this feature of the claim such as, identifying, by a theme identification module, a query theme based on aggregated query image feature data (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI). Moreover, as previously used, the Ghadekar’s transaction list in equation 1 can be understood to be a query theme, since it’s part of a querying process and indicating a theme of selection or querying goal, such as a transaction involves items being selected for buying or selling therefore, by BRI, it’s analogous to a query theme.
Moreover, regarding the limitation of “generating, by a demand analysis module, demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform,” the Applicants are respectfully reminded that the claims are construed based on BRI in light of the specification, therefore the bringing in of the specification’s to be the instant of scope of the claim is not persuasive, such as the Applicants alleging that the instant specification’s [0020] is used to then teach that “the claimed invention has its demand data at issue is based on comparing a query theme to a catalog data of an online platform and claim 1’s demand data is focused on certain produces or items that can be on demand from the perspective of a plurality of users” is not reflected in the claims.
Moreover, the claims simply recited “demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform” which the examiner finds Ghadekar to teach, in Ghadekar’s section VI discloses the output of the processing is to provide the user with recommendation of items based on the input image of what other users have had purchased in the past to follow interest rule of what items the current user may be interested in, therefore, it can be understood as a demand data has been generated of what users would be interested in of which products are being in demand according to the input image. Furthermore, this the demand data is based on comparing the input image to the images of what other users purchased in the past of catalog data of an online platform, by BRI, such as finding the similarity between user-user according to section VI; by BRI, covers the scope of the claim, since determining of similarity indicates a comparison.
Therefore, the prior art rejections remain.
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 1-20 are rejected under 35 U.S.C. 101
Regarding Independent Claim 1 and its dependent claims 2-9,
Step 1 Analysis: Claim 1 is directed to a method/process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, “generating multiple image tags for each query image of the plurality of query images; analyzing the multiple image tags based on a knowledge graph to create query image feature data for each query image; identifying a query theme based on aggregated query image feature data for the plurality of query images; generating demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform” The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. The limitations of:
“generating multiple image tags for each query image of the plurality of query images” is a step that a human can also perform, based on BRI (broadest reasonable interpretation), through process of observation and evaluation using pen and paper, such as, the human mind can observe image and generate image tags describing contents of the image using pen and paper; “analyzing the multiple image tags based on a knowledge graph to create query image feature data for each query image” is also a step a human can perform, based on BRI, such as the human can analyze image tags given based on certain condition and further provided information/data here being a knowledge graph to create further information here being query image feature data, using pen and paper; “identifying a query theme based on aggregated query image feature data for the plurality of query images” is a step a human can also perform, based on BRI, such as the human can observe data and identify a theme based on certain condition such as recited in the claim; “generating demand data indicating a level of user demand for an object based on comparing the query theme to catalog data of an online platform” is a step that the human mind can perform, based on BRI, such as the human mind can generate data/information using pen and paper to indicate user demand according to certain rule, condition; moreover, the wherein clause here is recited as a further specification limitation of further specifying how the data/information is obtained by another mental process here is comparing data/information which a human mind can perform, based on BRI, using pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
An image tagging module;
An image content analyzer module;
A theme identification module;
A demand analysis module;
The additional elements as shown are generic modules recited at high level of generality to perform generic functions hence, not indicative of an integration of the judicial exceptions into a practical application. The claim as a whole is directed to an abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as the additional elements indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-9 do not provide elements that overcome the deficiencies of the independent claim 1. Moreover, claim 2 recites, in part, “wherein at least one query image of the plurality of query images is a synthetic image generated by a generative artificial intelligence model” which is a further specification of what the query image is being an abstract idea, and an additional of generic, recited at high level of generality, well-known generative artificial intelligence model, not indicating an improvement. Claim 3 recites, in part, “receiving multiple query images associated with a user query on the online platform” is an additional element to be insignificant extra-solution activity of data gathering of receiving data/information, which is not an indication of an integration of the judicial exceptions into a practical application not considered significantly more, “wherein the multiple query images comprise image metadata comprising user action data associated with aspects of one or more of the multiple query images” is a wherein clauses of further specifying the data/information hence still, merely part of the data gathering insignificant extra-solution activity. Claims 4-5 recites, in part, wherein clauses to further specify the data/information hence, still merely data gathering insignificant extra-solution activities of data gathering as part of the mental processes they depend on as data providing, data specifying. Claim 6 recites, in part, “wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags” which is a wherein clauses of providing further specification to what the query image feature data comprises hence, still merely data/information as part of the data gathering being insignificant and part of the mental process performed on the data; “wherein analyzing the multiple image tags based on the knowledge graph associated with the online platform to create the query image feature data” which is a mental process, based on BRI, the human mind can analyze image information based on certain provided data/information to create further information according to certain rule, condition, using pen and paper; “mapping….knowledge graph” and “determining a semantic context….multiple image tags” to be steps of mental processes, based on BRI, such as the human mind can perform mapping data/information according to certain condition using pen and paper and determine data/information by observing certain data/information according to certain condition. Claim 7 recites, in part, “aggregating….image feature data; and storing…image feature data” include additional elements of insignificant extra-solution activities of data gathering of data processing including aggregating data/information to generate further data/information and storing data/information hence, not indicative of an integration of the judicial exceptions into a practical application, nor considered significantly more. Claim 8 recites, in part, “transmitting…..the demand data” and claim 9 recites, in part, “transmitting…the online platform” and “user computing device,….online platform,” all to be additional elements of generic components, devices performing generic functions and insignificant extra-solution activities of data gathering, data transmitting.
Accordingly, the dependent claims 2-9 are not patent eligible under 101.
Regarding independent claim 10 and its dependent claims 11-15:
Claim 10 recites analogous limitations to the independent claim 1 which are analyzed under the same approach as shown above to be 101 ineligible. The dependent claims 11-15 recites analogous limitations to the dependent claims 2-9 hence, are analyzed under the same approach as shown above to be 101 ineligible. Moreover claim 14 recites added feature including using clustering to create query clusters which a human mind can perform using a pen and paper to cluster together data according to certain condition of the data.
Regarding independent claim 16 and its dependent claims 17-20:
Claim 16 recites analogous limitations to the independent claim 1 which are analyzed under the same approach as shown above to be 101 ineligible. Moreover, claim 1 recites further additional elements of generic computer and computer components performing generic functions such as “non-transitory computer-readable medium, storing executable instructions, executed by processing device, which are not indicative of an integration of the judicial exceptions into a practical application nor considered significantly more. ”The dependent claims 17-20 recites analogous limitations to the dependent claims 2-9 hence, are analyzed under the same approach as shown above to be 101 ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-5, 7-8, 10-12, 14-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Premanand Ghadekar et. al. (“Image-Based Product Recommendations Using Market Basket Analysis, Sept. 2019, 2019 5th International Conference on Computing, Communication, Control And Automation” hereinafter as “Ghadekar”).
(as best understood based on the 112f interpretation) Regarding claim 1, Ghadekar discloses a method performed by one or more processing devices (abstract discloses the invention uses neural network which can be understood to include the use of processing devices such as computer), comprising: receiving, by an image tagging module, a plurality of query images corresponding to a plurality of user queries from a plurality of users during a period of time (section VII, 1st par., discloses a scenario where the user upload an image of a product to the smart engine and the smart engine recommend related products; therefore, the uploaded image here is analogous to the query image as claimed and the smart engine is analogous to the image tagging module as claimed, by BRI; moreover, in section VI’s “Collaborative Filtering User-User Similarity” section, it discloses the system identifies another user who is similar and has same interests as that of the current user include comparison involves liking of different users, therefore, this also indicates that the system has collected information regarding other user’s performing the same querying steps including a plurality of images have been updated for search during a period of time); generating, by the image tagging module, multiple image tags for each query image of the plurality of query images (section VII, 2nd par., discloses identifying distinct products in the image by a CNN, wherein the identified information include contents of the product [analogous to multiple image tags as claimed, by BRI, such as the labels given to each product in the image as shown in FIG. 1, therefore, when an image includes multiple products, in a scenario BRI scope, it would include multiple labels such as an image of a pencil and an eraser together such as shown in section V, 1st par.], as part of the smart engine moreover, in section VI’s “Collaborative Filtering User-User Similarity” section, it discloses the system identifies another user who is similar and has same interests as that of the current user include comparison involves liking of different users, therefore, this also indicates that the system has collected information regarding other user’s performing the same querying steps, it can be understood that the same image tags have been generated for these images as well); analyzing, by an image content analyzer module, the multiple image tags based on a knowledge graph to create query image feature data for each query image (section V, 1st 3 pars, discloses when the image labels are identified, the association rule mining is used to state association between the products in the image with another product likely to be bought or be interested in by the user, therefore, the association rule here can be understood as a knowledge graph as claimed, by BRI, and the association here can be understood to be the recited query image feature data as claimed, by BRI; and the machine learning for the associations rule mining here is analogous to the recited image content analyzer module as claimed, by BRI; the same feature data has been analyzed for other users’ steps as well); identifying, by a theme identification module, a query theme based on aggregated query image feature data for the plurality of query images (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI; the same identifying step can be understood to be performed for other users’ performance of the system as well); and generating, by a demand analysis module, demand data indicating a level of user demand for an object (section VI discloses the output of the processing is to provide the user with recommendation of items based on the input image of what other users have had purchased in the past to follow interest rule of what items the current user may be interested in, therefore, it can be understood as a demand data has been generated of what users would be interested in of which products are being in demand according to the input image, moreover, the recommendation here is based on the output of equation 1 of section V; ; the processor with program to perform this steps can be understood to be analogous to the recited demand analysis module as claimed, by BRI; any user demand would be indicating a level of demand such as being analogous to the recited “indicating a level of user demand”) based on comparing the query theme to catalog data of an online platform (the demand data is based on comparing the input image to the images of what other users purchased in the past of catalog data of an online platform, by BRI, such as finding the similarity between user-user according to section VI; by BRI, covers the scope of the claim, since determining of similarity indicates a comparison).
(as best understood based on the 112f interpretation) Regarding claim 3, Ghadekar discloses the method of claim 1, further comprising receiving multiple query images associated with a user query on the online platform (since the processing of Ghadekar is based on other users’ clicked images or input images to generate the associations rule for recommendation based on user-user similarity as disclosed in section VI; therefore, it can be understood to have the use of receiving multiple query images associated with other user query on the online platform, by BRI, covers the scope of the claim), wherein the multiple query images comprise image metadata comprising user action data associated with aspects of one or more of the multiple query images (since the multiple query images are images of other users’ clicked images or purchasing images or input images, hence, they indicates metadata associated with multiple query images, by BRI, covers the scope of the claimed limitation).
Regarding claim 4, Ghadekar discloses the method of claim 1, wherein the multiple image tags comprise object categories, object names, and object attributes (the product contents and labels being image tags, as discussed previously in claim 1, such as shown in FIG. 1 of object names/labels, and confidence value [such as shown in FIG. 6 which can be understood to be object attributes, by BRI, since a confidence value is an attribute of the object in the image]; moreover, the output also include an association rule such as disclosed in section IX, 1st 2 pars, which can be understood to be object category as claimed, by BRI).
Regarding claim 5, Ghadekar discloses the method of claim 1, wherein the online platform is a marketplace platform (section I, 1st par., discloses the platform being e-commerce website), and wherein the knowledge graph comprises product information for products associated with the marketplace platform (as discussed above in claim 1, the knowledge graph include product information as association information between products from the marketplace platform, by BRI, covers the scope of the claim).
Regarding claim 7, Ghadekar discloses the method of claim 1, further comprising: aggregating the query image feature data for the plurality of query images corresponding to the plurality of user queries to generate the aggregated query image feature data (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI; the items from the list is based on images from other users’ queries); and storing the aggregated query image feature data (processing the aggregated data indicates a storing of the data, by BRI, covers the scope of the claimed limitation).
Regarding claim 8, Ghadekar discloses the method of claim 1, further comprising transmitting the demand data to the online platform (the recommendation as discussed previously in claim 1 is being shown to the user using the online shopping website, hence, it can be understood as the demand data being transmitted to the website to show to the user), wherein the demand data comprises a synthetic object image generated based on the demand data (the recommendation data includes images output by the system [generated from the processing has been mentioned] as shown in FIG. 7).
(as best understood based on the 112f interpretation) Regarding claim 10, Ghadekar discloses a system, comprising (abstract discloses the invention uses neural network which can be understood to include the use of processing devices as a system such as computer): an image tagging module configured to generate multiple image tags for each query image of a plurality of query images corresponding to a plurality of user queries from a plurality of users during a period of time (section VII, 1st par., discloses a scenario where the user upload an image of a product to the smart engine and the smart engine recommend related products; therefore, the uploaded image here is analogous to the query image as claimed and the smart engine is analogous to the image tagging module as claimed, by BRI; moreover, in section VI’s “Collaborative Filtering User-User Similarity” section, it discloses the system identifies another user who is similar and has same interests as that of the current user include comparison involves liking of different users, therefore, this also indicates that the system has collected information regarding other user’s performing the same querying steps including a plurality of images have been updated for search during a period of time); an image content analyzer module configured to analyze the multiple image tags based on a knowledge graph to create query image feature data from each query image (section VII, 2nd par., discloses identifying distinct products in the image by a CNN, wherein the identified information include contents of the product [analogous to multiple image tags as claimed, by BRI, such as the labels given to each product in the image as shown in FIG. 1, therefore, when an image includes multiple products, in a scenario BRI scope, it would include multiple labels such as an image of a pencil and an eraser together such as shown in section V, 1st par.], as part of the smart engine; section V, 1st 3 pars, discloses when the image labels are identified, the association rule mining is used to state association between the products in the image with another product likely to be bought or be interested in by the user, therefore, the association rule here can be understood as a knowledge graph as claimed, by BRI, and the association here can be understood to be the recited query image feature data as claimed, by BRI; and the machine learning for the associations rule mining here is analogous to the recited image content analyzer module as claimed, by BRI; moreover, in section VI’s “Collaborative Filtering User-User Similarity” section, it discloses the system identifies another user who is similar and has same interests as that of the current user include comparison involves liking of different users, therefore, this also indicates that the system has collected information regarding other user’s performing the same querying steps, it can be understood that the same image tags have been generated for these images as well); a theme identification module configured to identify a query theme based on aggregated query image feature data for the plurality of query images (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI; ; the same feature data has been analyzed for other users’ steps as well); and a demand detection module configured to generate demand data indicating user demand for an object based on comparing the query theme to catalog data of an online platform (section VI discloses the output of the processing is to provide the user with recommendation of items based on the input image of what other users have had purchased in the past to follow interest rule of what items the current user may be interested in, therefore, it can be understood as a demand data has been generated of what users would be interested in of which products are being in demand according to the input image, moreover, the recommendation here is based on the output of equation 1 of section V; the processor with program to perform this steps can be understood to be analogous to the recited demand detection module as claimed, by BRI; any user demand would be indicating a level of demand such as being analogous to the recited “indicating a level of user demand”).
Regarding claim 11, Ghadekar discloses the system of claim 10, wherein the demand detection module is further configured to: determine a query result by comparing each query image associated with a user query to the catalog data of the online platform (the demand data is based on comparing the input image to the images of what other users purchased in the past of catalog data of an online platform, by BRI, such as finding the similarity between user-user according to section VI; by BRI, covers the scope of the claim, since determining of similarity indicates a comparison); and provide the query result to a user computing device associated with the user query (the recommendation result is shown to the user at the website).
(as best understood based on the 112f interpretation) Regarding claim 12, Ghadekar discloses the system of claim 10, wherein the image tagging module is configured to receive multiple query images associated with a user query on the online platform (since the processing of Ghadekar is based on other users’ clicked images or input images to generate the associations rule for recommendation based on user-user similarity as disclosed in section VI; therefore, it can be understood to have the use of receiving multiple query images associated with other user query on the online platform, by BRI, covers the scope of the claim), wherein the multiple query images comprise image metadata comprising user action data associated with aspects of one or more of the multiple query images (since the multiple query images are images of other users’ clicked images or purchasing images or input images, hence, they indicates metadata associated with multiple query images, by BRI, covers the scope of the claimed limitation).
(as best understood based on the 112f interpretation) Regarding claim 14, Ghadekar discloses the system of claim 10, wherein the theme identification module comprises a clustering model configured to cluster the aggregated query image feature data to one or more query clusters, wherein the one or more query clusters correspond to one or more query themes (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI; the items from the list is based on images from other users’ queries, according to the association rule [according to the query them as claimed]; wherein the recommendation processing is based on a support calculation such as shown in equation 2 of section V, hence, indicates a clustering technique, based on BRI, covers the scope of the claimed limitation, to create query clusters such as shown section V, last par. of the support associations).
(as best understood based on the 112f interpretation) Regarding claim 15, Ghadekar discloses the system of claim 10, wherein the demand detection module is further configured to transmit the demand data to the online platform (the recommendation as discussed previously in claim 1 is being shown to the user using the online shopping website, hence, it can be understood as the demand data being transmitted to the website to show to the user), wherein the demand data comprises a synthetic object image generated based on the demand data (the recommendation data includes images output by the system [generated from the processing has been mentioned] as shown in FIG. 7).
(as best understood based on the 112f interpretation) Regarding claim 16, Ghadekar discloses a non-transitory computer-readable medium, storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising (abstract discloses the invention uses neural network which can be understood to include the use of processing devices such as computer which includes a process to execute instructions of the invention stored in a non-transitory storage medium such as a ROM or RAM), receiving a plurality of query images corresponding to a plurality of user queries from a plurality of users via an online platform (section VII, 1st par., discloses a scenario where the user upload an image of a product to the smart engine and the smart engine recommend related products; therefore, the uploaded image here is analogous to the query image as claimed and the smart engine is analogous to the image tagging module as claimed, by BRI, of an online platform e-commerce website according to sect. I, 1st par.; moreover, in section VI’s “Collaborative Filtering User-User Similarity” section, it discloses the system identifies another user who is similar and has same interests as that of the current user include comparison involves liking of different users, therefore, this also indicates that the system has collected information regarding other user’s performing the same querying steps including a plurality of images have been updated for search during a period of time); generating multiple image tags for each query image of the plurality of query images (section VII, 2nd par., discloses identifying distinct products in the image by a CNN, wherein the identified information include contents of the product [analogous to multiple image tags as claimed, by BRI, such as the labels given to each product in the image as shown in FIG. 1, therefore, when an image includes multiple products, in a scenario BRI scope, it would include multiple labels such as an image of a pencil and an eraser together such as shown in section V, 1st par.], as part of the smart engine); analyzing the multiple image tags based on a knowledge graph to create query image feature data for each query image (section V, 1st 3 pars, discloses when the image labels are identified, the association rule mining is used to state association between the products in the image with another product likely to be bought or be interested in by the user, therefore, the association rule here can be understood as a knowledge graph as claimed, by BRI, and the association here can be understood to be the recited query image feature data as claimed, by BRI; and the machine learning for the associations rule mining here is analogous to the recited image content analyzer module as claimed, by BRI); identifying a query theme based on aggregated query image feature data for the plurality of query images (the association as discussed previously, as disclosed in section V, 1st 2 paragraphs, to provide transaction list of what to be recommended to the user such as the list of equation (1) in section V, therefore, the list here can be understood to be a query theme as claimed, by BRI, based on aggregated query image feature data, since each item in the list if obtained from an association, therefore, the list includes aggregated list of associated items to be recommended to the user, by BRI, covers the scope of the claimed limitation; the processor programed to execute this step is analogous to the theme identification module as claimed, by BRI); and generating demand data indicating a level of user demand for an object corresponding to the query theme (section VI discloses the output of the processing is to provide the user with recommendation of items based on the input image of what other users have had purchased in the past to follow interest rule of what items the current user may be interested in, therefore, it can be understood as a demand data has been generated of what users would be interested in of which products are being in demand according to the input image, moreover, the recommendation here is based on the output of equation 1 of section V; ; the processor with program to perform this steps can be understood to be analogous to the recited demand analysis module as claimed, by BR).
Regarding claim 17, Ghadekar discloses the non-transitory computer-readable medium of claim 16, wherein the plurality of query images comprises image metadata comprising user action data associated with aspects of the plurality of query images (since the processing of Ghadekar is based on other users’ clicked images or input images to generate the associations rule for recommendation based on user-user similarity as disclosed in section VI; therefore, it can be understood to have the use of receiving multiple query images associated with other user query on the online platform, by BRI, covers the scope of the claim; since the multiple query images are images of other users’ clicked images or purchasing images or input images, hence, they indicates metadata associated with multiple query images, by BRI, covers the scope of the claimed limitation).
Regarding claim 18, Ghadekar discloses the non-transitory computer-readable medium of claim 16, wherein the executable instructions, which when executed by a processing device, cause the processing device to perform further operations comprising: determining a query result by comparing one or more query images corresponding to a user query to catalog data of the online platform (the demand data is based on comparing the input image to the images of what other users purchased in the past of catalog data of an online platform, by BRI, such as finding the similarity between user-user according to section VI; by BRI, covers the scope of the claim, since determining of similarity indicates a comparison); and providing the query result to a user computing device associated with user query (the recommendation result is shown to the user at the website).
Regarding claim 20, Ghadekar discloses the non-transitory computer-readable medium of claim 16, wherein the executable instructions, which when executed by a processing device, cause the processing device to perform further operations comprising: transmitting the demand data to the online platform (the recommendation as discussed previously in claim 1 is being shown to the user using the online shopping website, hence, it can be understood as the demand data being transmitted to the website to show to the user), wherein the demand data comprises a synthetic object image generated based on the demand data (the recommendation data includes images output by the system [generated from the processing has been mentioned] as shown in FIG. 7).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Premanand Ghadekar et. al. (“Image-Based Product Recommendations Using Market Basket Analysis, Sept. 2019, 2019 5th International Conference on Computing, Communication, Control And Automation” hereinafter as “Ghadekar”) in view of Tingting Qiao et. al. (“MirrorGAN: Learning Text-To-Image Generation by Redescription, 2019, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR, pp. 1505-1514” hereinafter as “Qiao”).
Regarding claim 2, Ghadekar discloses the method of claim 1, wherein at least one query image of the plurality of query images (as discussed above in claim 1).
However, Ghadekar does not explicitly disclose at least one query image of the plurality of query images is a synthetic image generated by a generative artificial intelligence model.
In the same field of image processing (title, Qiao), Qiao discloses at least one query image of the plurality of query images is a synthetic image generated by a generative artificial intelligence model (FIG. 2 discloses that an image can be generated based on giving a description, therefore, the query image of Ghadakar can be understood to be an image given by description by the user, the image generated by a neural network to generate images [being generative]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Ghadekar to perform receiving query image, wherein the query image is a synthetic image generated by a generative artificial intelligence model as taught by Qiao to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to generate accurate image based on description (abstract, Qiao).
Claims 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Premanand Ghadekar et. al. (“Image-Based Product Recommendations Using Market Basket Analysis, Sept. 2019, 2019 5th International Conference on Computing, Communication, Control And Automation” hereinafter as “Ghadekar”) in view of Thomas Deselaers et. al. (“Visual and Semantic Similarity in ImageNet, June 2011, CVPR 2011, Colorado Springs, CO, USA” hereinafter as “Deselaers”).
(as best understood based on the 112f interpretation) Regarding claim 6, Ghadekar discloses the method of claim 1, wherein analyzing, by the image content analyzer module, the multiple image tags based on the knowledge graph associated with the online platform to create the query image feature data for each query image comprises (as discussed above in claim 1): mapping the multiple image tags to the knowledge graph (mapping the labeled item with the association rule such as shown by arrow in section V of an example of the association between the data, by BRI, covers the scope of the claimed limitation); the multiple image tags based on the knowledge graph (as discussed above in claim 1, the image tags based on the knowledge graph).
However, Ghadekar does not explicitly disclose wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determining a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags.
In the same field of image similarity determination (title, Deselaers) Deselaers discloses wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags (FIG. 6 discloses that the semantic distance between the categories of the neighbors are determined to show objects of the same basic-level category, hence, by BRI, covers the scope of the claimed limitation wherein the labels here indicate semantic descriptions being inferred), and determining a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags (FIG. 6 discloses the semantic context being used to obtain the inferred semantic descriptions being the labels of the multiple image tags [the image labels]; by BRI, covers the scope of the claim, further discloses in section 3).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Ghadekar to perform analyzing, by the image content analyzer module, the multiple image tags based on the knowledge graph associated with the online platform to create the query image feature data comprises: mapping the multiple image tags to the knowledge graph; wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determining a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags as taught by Deselaers to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to perform visual similarity determination effectively (abstract, Deselaers).
Regarding claim 13, Ghadekar discloses the system of claim 10, wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, wherein the image content analyzer module is further configured to: (as discussed above in claim 10): map the multiple image tags to the knowledge graph (mapping the labeled item with the association rule such as shown by arrow in section V of an example of the association between the data, by BRI, covers the scope of the claimed limitation); the multiple image tags based on the knowledge graph (as discussed above in claim 10, the image tags based on the knowledge graph).
However, Ghadekar does not explicitly disclose wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determine a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags.
In the same field of image similarity determination (title, Deselaers) Deselaers discloses wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags (FIG. 6 discloses that the semantic distance between the categories of the neighbors are determined to show objects of the same basic-level category, hence, by BRI, covers the scope of the claimed limitation wherein the labels here indicate semantic descriptions being inferred), and determine a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags (FIG. 6 discloses the semantic context being used to obtain the inferred semantic descriptions being the labels of the multiple image tags [the image labels]; by BRI, covers the scope of the claim, further discloses in section 3).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Ghadekar to perform analyzing, by the image content analyzer module, the multiple image tags based on the knowledge graph associated with the online platform to create the query image feature data comprises: mapping the multiple image tags to the knowledge graph; wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determining a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags as taught by Deselaers to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to perform visual similarity determination effectively (abstract, Deselaers).
Regarding claim 19, Ghadekar discloses the non-transitory computer-readable medium of claim 16, wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, wherein the executable instructions, which when executed by a processing device, cause the processing device to perform further operations comprising: (as discussed above in claim 16): map the multiple image tags to the knowledge graph (mapping the labeled item with the association rule such as shown by arrow in section V of an example of the association between the data, by BRI, covers the scope of the claimed limitation); the multiple image tags based on the knowledge graph (as discussed above in claim 16, the image tags based on the knowledge graph).
However, Ghadekar does not explicitly disclose wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determine a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags.
In the same field of image similarity determination (title, Deselaers) Deselaers discloses wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags (FIG. 6 discloses that the semantic distance between the categories of the neighbors are determined to show objects of the same basic-level category, hence, by BRI, covers the scope of the claimed limitation wherein the labels here indicate semantic descriptions being inferred), and determine a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags (FIG. 6 discloses the semantic context being used to obtain the inferred semantic descriptions being the labels of the multiple image tags [the image labels]; by BRI, covers the scope of the claim, further discloses in section 3).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Ghadekar to perform analyzing, by the image content analyzer module, the multiple image tags based on the knowledge graph associated with the online platform to create the query image feature data comprises: mapping the multiple image tags to the knowledge graph; wherein the query image feature data comprises inferred semantic descriptions of the multiple image tags, and determining a semantic context of the multiple image tags to obtain the inferred semantic descriptions of the multiple image tags as taught by Deselaers to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to perform visual similarity determination effectively (abstract, Deselaers).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Premanand Ghadekar et. al. (“Image-Based Product Recommendations Using Market Basket Analysis, Sept. 2019, 2019 5th International Conference on Computing, Communication, Control And Automation” hereinafter as “Ghadekar”) in view of Yucheng Jin et. al. (“CircleBuy: a visual search based second screen application of buying products in videos, June 2016, Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 287-292” hereinafter as “Jin”).
Regarding claim 9, Ghadekar discloses the method of claim 1 (as discussed above in claim 1).
However, Ghadekar does not explicitly disclose further comprising transmitting to a user computing device a notification message indicating that the object has been newly added to the online platform.
In the same field of e-commerce image querying (title and abstract, Jin) Jin discloses further comprising transmitting to a to a user computing device a notification message indicating that the object has been newly added to the online platform (page 289, 1st col., last par., discloses the similar product also is given descriptions [notification message] since, Ghadekar discloses the similar products are given to the user by transmitting it to the computing device, here, Jin further teaches the similar can further be given description [message] and the showing to the user the description is analogous to notification as claimed, by BRI, covers the scope of the claimed limitation).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Ghadekar to have an online platform processing further comprising transmitting to a to a user computing device a notification message indicating that the object has been newly added to the online platform as taught by Jin to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to provide interactive and effective user experience (page 289, 1st col., last par.).
Conclusion
THIS ACTION IS MADE FINAL. 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 PHUONG HAU CAI whose telephone number is (571)272-9424. The examiner can normally be reached M-F 8:30 am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
Prosecution Insights