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 Amendment
Receipt is acknowledged of claim amendments with associated arguments/remarks, received April 16, 2026. Claims 1, 5-9, 13-21 are pending in which claims 1, 17 were amended. Claims 2-4, 10-12 were cancelled.
Response to Arguments
Applicant’s argument, see pg 7-13, filed April 16, 2026, with respect rejections of claims 1, 5-9, 13-21 under 35 U.S.C. § 103 has been fully considered but is not persuasive.
Applicant argues “the cited combination does not teach, suggest or provided any motivation for the claimed method which requires a purely verbal, non-visual user conception to a first machine- processable visual search basis for retrieving real products in a network” (Remarks – 04/16/2026, pg 9 ln 7-9).
Claim 1 recites “generating a plurality of image representations of a fictitious non-existing object with aid of linguistic or textual specifications for an image generation program of an electronic data processing system with Artificial Intelligence.”
Claim 1 explicitly claims “linguistic or textual specifications” and therefore the argument is not accurate with stating the claimed method “requires a purely verbal, non-visual user conception.” Independent claim 17 likewise recites the same limitation in parallel to claim 1 as directed to “user-generated linguistic or textual specifications input”.
Respectfully, the examiner is not persuaded.
Applicant argues the applicant’s invention discloses the prior art Surya et al (US 10,713,821 B1) and Yada et al (US 2020/03566591) generates images using a machine learning system trained based on source images. Examiner acknowledges Surya et al states “a machine learning system is described that generates image data representative of text strings (e.g., search strings).” (Surya et al, col 2 ln 5-7). Likewise, Yada et al states the invention discloses the “technique generates the query image based on one or more user-supplied input images and/or one or more user-supplied information items that describe at least one desired characteristic of the query image” (Yada et al ¶ [0002]).
Applicant argues the applicant’s disclose teaches the generation of image data with the absence of any available source material with citation to the applicant’s specification [0007] for support, which recites: [0007] It is the object of the invention to enable a search for items or products in a network when no images of the item or product are available.
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Surya et al and Yada et al each teach generation of synthetic images based on inputs from the user and using such synthetic images to perform query searches. Applicant provides no specific details of how the applicant’s invention generates fictitious-object images to enable a query search to distinguish the claimed invention from the prior art.
Furthermore, Applicant’s disclosure does not provide substantive detail regarding how a given artificial intelligence model is designed or trained to perform such generative modeling. Furthermore, the applicant’s drawings do not provide substantive details in what the model is or how it performs the function of generating image data based on mere linguistic or textual specifications (applicant’s disclosed Figure shown).
Respectfully, the examiner is not persuaded.
Applicant further contends “that it is an error, in an obviousness analysis, to simply ignore the no-source-image bridge limitation as discussed above into separate general propositions that synthetic images can be generated and that query images can be used for retrieval, because the present claims are directed to the specific technical transition that enables an image-based search to begin where otherwise no such search could even be started.” (Remarks – 04/16/2026 pg 9 ln 14-18). It appears that applicant is alluding to the concept of mere creative thought, which is not patent eligible (see MPEP § 2106 for guidance on subject matter eligibility).
Respectfully, the argument is not persuasive.
Applicant further argues the disclosed invention should be considered based on the date of the disclosure when analyzing the prior art for obviousness (Remarks – 04/16/2026 pg 9 ln 14-pg 10 ln 15). The applicant’s invention has a foreign priority date of 06/09/2021. Cited prior art Surya et al (US 10,713,821 B1) has a filing date 06/27/2019 and publication 07/14/2020 and Yada et al (US 2020/03566591) has a filing date 05/09/2019 and publication 11/12/2020. Examiner maintains it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to combine the teachings of Surya et with Yada et al to produce the equivalent claimed invention of the applicant.
Respectfully, the examiner is not persuaded.
Applicant further argues the prior art does not teach generating “a plurality of image representations of a fictitious non-existing object” (independent claim 1, 17 limitation) as summarized in arguments (Remarks – 04/16/2026 pg 10 ln 16 – pg 12 ln 9). As discussed previously in office actions and above, the image data generated by Surya and Yada are synthetic images, thereby equivalent to applicant’s argument of the images not available to the user.
Respectfully, the examiner is not persuaded.
Applicant additionally argues prior art Walters et al (US 2020/0226807) does not cure the deficiencies set forth in applicant’s arguments pertaining to Surya et al (US 10,713,821 B1) and Yada et al (US 2020/03566591) (Remarks – 04/16/2026 pg 12 ln 10 – pg 13 ln 2). For the reasons provided above, the examiner is, respectfully, not persuaded by the applicant.
No further argument is presented.
All arguments were addressed.
Claim Objections
Claims 1, 17 objected to because of the following informalities:
Claim 1, 17 were each amended to recite in the preamble “of which products no images are available to a user,” and may have been meant as “of which no product images are available to a user”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 13-17, 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Surya et al (US 10713821, previously cited 08/20/2024)) in view of Yada et al (US 2020/0356591, previously cited 08/20/2024)).
Regarding Claim 1, Surya et al teach a method for finding products in a network (generating synthetic images of products; Fig 4 and col 11 ln 15-22) of which products no images are available to a user (generating synthetic images are images of objects distinct from products previously shown (products without images because the images are new and based on text modification from user); col 2 ln 24-39), comprising the steps of:
generating a plurality of image representations of a fictitious non-existing object (applicant discloses generation of a ‘fictitious non-existing object’ is known in the art, specification ¶ [0008]-[0010] and Fig 1) with aid of linguistic or textual specifications for an image generation program of an electronic data processing system with Artificial Intelligence (an input search query 402 (text specifications) are input to generate synthetic images 408, 410 412 using the stage-I and stage-II GANs (AI image generation); Figs 1-4 and col 11 ln 15-22),
which generated plurality of image representations each contain all of the specifications of the fictitious non-existing object (the synthesized images 408, 410, 412 each demonstrate variations of the text input features 402; Fig 4 and col 11 ln 15-22);
displaying the generated plurality of image representations on a display device of the electronic data processing system of the user (the synthesized images 408, 410, 412 from generators 150, 250 are displayed to a user on a display component 306; Figs 3, 4 and col 9 ln 41-50);
subjecting at least one of the generated plurality of image representations selected by the user to an image analysis by the electronic data processing system (a particular representation of object of interest may be selected for further analysis, such as by a user with a touch-sensitive display 306; col 8 ln 24-31, col 10 ln 3-8).
Surya et al does not teach searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis; and displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user.
Yada et al is analogous art pertinent to the technological problem addressed in this application and teaches searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis (the generated image Ig according to user specifications is the query image input to retrieval component 110 with a search engine 112 and index 114 to search for similar real object images (products) based on the synthetic query image; Figs 1, 4 and ¶ [0041]-[0045], [0058]); and
displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user (candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user by the retrieval component 110 on the display 1822 of user device 1802; Figs 1, 18 and ¶ [0046], [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to combine the teachings of Surya et al with Yada et al including searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis; and displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user. By allowing a user to craft a custom query image for a search, a consumer can generate the object of interest prior to searching for the product, thereby streamlining the process to search, as recognized by Yada et al (¶ [0001]-[0002]).
Regarding Claim 13, Surya et al in view of Yada et al teach the method according to claim 1 (as described above), wherein the at least one of the generated plurality of image representations selected by the user for the image analysis is displayed on the display device next to the at least one identical or similar real product found as result of the searching (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user by the retrieval component 110 on the display 1822 of user device 1802; Figs 1, 3, 4, 18 and ¶ [0046]-[0047], [0120]).
Regarding Claim 14, Surya et al in view of Yada et al teach the method according to claim 1 (as described above), wherein the image analysis comprises a similarity analysis of the at least one of the generated plurality of image representations selected by the user for the image analysis (Yada et al, the generated image Ig is searched for one or more similar candidate images in the search engine 112 which are then retrieved by the retrieval component and presented to the user via a user interface; Figs 3, 4 and ¶ [0044]-[0047]).
Regarding Claim 15, Surya et al in view of Yada et al teach the method according to claim 1 (as described above), wherein the at least one identical or similar real product found by the searching is selectable with an operating pointer to obtain further information about the at least one identical or similar real product (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user on the display 1822, which can be a GUI 1824 of user device 1802, and the user can then select a link about a product using either a mouse or the GUI to input and learn more information about the product; Figs 1, 4, 18 and ¶ [0049], [0063], [0120]).
Regarding Claim 16, Surya et al in view of Yada et al teach the method according to claim 1 (as described above), wherein a plurality of identical or similar real products are found by the searching and are displayed on the display device according to predeterminable criteria (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user on the display 1822, which ca be further refined with the item selection component 118; Figs 1, 4, 18 and ¶ [0049], [0063], [0120]).
Regarding Claim 17, Surya et al teach a method for finding products in a network (generating synthetic images of products; Fig 4 and col 11 ln 15-22) of which products no images are available to a user (generating synthetic images are images of objects distinct from products previously shown (products without images because the images are new and based on text modification from user); col 2 ln 24-39), comprising the steps of:
generating a plurality of image representations of a fictitious non-existing object (applicant describes an example ‘fictitious non-existing object’ as a table, specification ¶ [0010] and Fig 1) with aid of linguistic or textual specifications for an image generation program of an electronic data processing system with Artificial Intelligence (an input search query 402 (text specifications) are input to generate synthetic images 408, 410 412 using the stage-I and stage-II GANs (AI image generation); Figs 1-4 and col 11 ln 15-22),
which generated plurality of image representations each contain all of the specifications of the fictitious non-existing object (the synthesized images 408, 410, 412 each demonstrate variations of the text input features 402; Fig 4 and col 11 ln 15-22);
displaying the generated plurality of image representations together on a display device of the electronic data processing system of a user (the synthesized images 408, 410, 412 from generators 150, 250 are displayed to a user on a display component 306; Figs 3, 4 and col 9 ln 41-50);
subjecting at least one of the generated plurality of image representations selected by the user to an image analysis by the electronic data processing system (a particular representation of object of interest may be selected for further analysis, such as with a touch-sensitive display 306; col 8 ln 24-31, col 10 ln 3-8).
Surya et al does not teach searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis; and displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user.
Yada et al is analogous art pertinent to the technological problem addressed in this application and teaches searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis (the generated image Ig according to user specifications is the query image input to retrieval component 110 with a search engine 112 and index 114 to search for similar real object images (products) based on the synthetic query image; Figs 1, 4 and ¶ [0041]-[0045], [0058]); and
displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user (candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user by the retrieval component 110 on the display 1822 of user device 1802; Figs 1, 18 and ¶ [0046], [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to combine the teachings of Surya et al with Yada et al including searching with the electronic data processing system for identical or similar real products in the network with a result of the image analysis; and displaying at least one identical or similar real product, found as a result of the searching, on the display device of the electronic data processing system of the user. By allowing a user to craft a custom query image for a search, a consumer can generate the object of interest prior to searching for the product, thereby streamlining the process to search, as recognized by Yada (¶ [0001]-[0002]).
Regarding Claim 19, Surya et al in view of Yada et al teach the method according to claim 17 (as described above), wherein the at least one of the generated plurality of image representations selected by the user for the image analysis is displayed on the display device next to the at least one identical or similar real product found as result of the searching (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user by the retrieval component 110 on the display 1822 of user device 1802; Figs 1, 3, 4, 18 and ¶ [0046]-[0047], [0120]).
Regarding Claim 20, Surya et al in view of Yada et al teach the method according to claim 17 (as described above), wherein the image analysis comprises a similarity analysis of the at least one of the generated plurality of image representations selected by the user for the image analysis (Yada et al, the generated image Ig is searched for one or more similar candidate images in the search engine 112 which are then retrieved by the retrieval component and presented to the user via a user interface; Figs 3, 4 and ¶ [0044]-[0047]).
Regarding Claim 21, Surya et al in view of Yada et al teach the method according to claim 17 (as described above), wherein a plurality of identical or similar real products are found by the searching and are displayed on the display device according to predeterminable criteria (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user on the display 1822, which ca be further refined with the item selection component 118; Figs 1, 4, 18 and ¶ [0049], [0063], [0120]).
Claims 5-9, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Surya et al (US 10713821, previously cited 08/20/2024)) in view of Yada et al (US 2020/0356591, previously cited 08/20/2024)) and Walters et al (US 2020/0226807, previously cited 08/20/2024)).
Regarding Claim 5, Surya et al in view of Yada et al teach the method according to claim 1 (as described above).
Surya et al in view of Yada et al do not teach wherein several of the generated plurality of image representations of non-existing objects are selectable and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects.
Walters et al is analogous art pertinent to the technological problem addressed in this application and teaches wherein several of the generated plurality of image representations of non-existing objects are selectable (image creation logic circuitry 1015 may select one or more system architectures 1020, 1022, 1024, which each generate different synthetic images (synthetic images are equivalent to described plurality of image representations of non-existing objects); Figs 1A, 1E and ¶ [0040]) and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects (selected models synthetic images are combined with the template 1045, based on customer device 1040 selected parameter set, to create a new image 1055; Figs 1A, 1E and ¶ [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to combine the teachings of Yada et al and Surya et al with Walters et al including wherein several of the generated plurality of image representations of non-existing objects are selectable and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects. By combining multiple synthetic images a unique iterative synthetic image is generated, which may be used to better train the neural network, thereby improve the efficiency of the generator and discriminator, as recognized by Walters et al (¶ [0022]-[0023]).
Regarding Claim 6, Surya et al in view of Yada et al and Walters et al teach the method according to claim 5 (as described above), wherein the at least one generated pictorial representation of the non-existing object is displayed on the display device next to a display of the at least one identical or similar real product (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user by the retrieval component 110 on the display 1822 of user device 1802; Figs 1, 4, 18 and ¶ [0046], [0120]).
Regarding Claim 7, Surya et al in view of Yada et al and Walters et al teach the method according to claim 6 (as described above), wherein the image analysis comprises a similarity analysis of the at least one pictorial representation of the non-existing object (Yada et al, the generated image Ig can be further mixed by fixed weights or by the user to further modify the synthetic image by refining the image to generate more or less similarity to select features based on the user preference; Figs 1, 3, 6-8 and ¶ [0041], [0057]-[0058], [0067]-[0076]).
Regarding Claim 8, Surya et al in view of Yada et al and Walters et al teach the method according to claim 7 (as described above), wherein the at least one identical or similar real product found by the searching is selectable with an operating pointer to obtain further information about the at least one identical or similar real product (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user on the display 1822, which can be a GUI 1824 of user device 1802, and the user can then select a link about a product using either a mouse or the GUI to input and learn more information about the product; Figs 1, 4, 18 and ¶ [0049], [0063], [0120]).
Regarding Claim 9, Surya et al in view of Yada et al and Walters et al teach the method according to claim 8 (as described above), wherein a plurality of identical or similar real products are found by the searching and are displayed on the display device according to predeterminable criteria (Yada et al, candidate images of real objects that match the query image representing the synthetic object image searched are displayed to the user on the display 1822, which ca be further refined with the item selection component 118; Figs 1, 4, 18 and ¶ [0049], [0063], [0120]).
Regarding Claim 18, Surya et al in view of Yada et al teach the method according to claim 17 (as described above).
Surya et al in view of Yada et al do not teach wherein several of the generated plurality of image representations of non-existing objects are selectable and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects.
Walters et al is analogous art pertinent to the technological problem addressed in this application and teaches wherein several of the generated plurality of image representations of non-existing objects are selectable (image creation logic circuitry 1015 may select one or more system architectures 1020, 1022, 1024, which each generate different synthetic images (synthetic images are equivalent to described plurality of image representations of non-existing objects); Figs 1A, 1E and ¶ [0040]) and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects (selected models synthetic images are combined with the template 1045, based on customer device 1040 selected parameter set, to create a new image 1055; Figs 1A, 1E and ¶ [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to combine the teachings of Yada et al and Surya et al with Walters et al including wherein several of the generated plurality of image representations of non-existing objects are selectable and at least one new pictorial representation of a non-existing object is generated from the selected generated plurality of image representations of non-existing objects. By combining multiple synthetic images a unique iterative synthetic image is generated, which may be used to better train the neural network, thereby improve the efficiency of the generator and discriminator, as recognized by Walters et al (¶ [0022]-[0023]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jin et al (US 2018/0357519, previously cited 08/20/2024) teaches a system and method for generating an image based on a description of an object and then searching for a similar image of the object generated in a database with multiple results displayed to a user.
Martinez et al (US 11568576, previously cited 08/20/2024) teaches techniques for generation of photorealistic synthetic image data based on use of a generator and discriminator network which can be used towards generation of images of with products for a customer to evaluate the product.
Bedi et al (US 2022/0101578, previously cited 08/20/2024) teaches a method, system and computer readable media for generating a composite image based on a generated fixed object in the image.
Pinel et al (US 2020/0167832, previously cited 08/20/2024) teaches a system and method for generating advertisements based on a compilation of multiple layouts which can be altered based on user preference.
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 KATHLEEN M BROUGHTON whose telephone number is (571)270-7380. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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