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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 16, 2026 has been entered.
Response to Amendment
Receipt is acknowledged of claim amendments with associated arguments/remarks, received September 16, 2026. Claims 1, 5-9, 13-22 are pending in which claims 1, 5-9, 15-18, 21 were amended and claim 22 is new. Claims 2-4, 10-12 were previously cancelled.
Response to Arguments
Applicant’s argument, see pg 6, filed September 16, 2026, with respect objections of claims 1, 17 for minor typographic errors has been fully considered and, by way of amendment, is persuasive. Therefore, the objections are withdrawn.
Applicant’s argument, see pg 6-8, filed September 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 prior art does not teach “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” in independent claim 1 (Remarks – 09/16/2026, pg 6-8) with the argument focused on the user input to select the synthetic image data. Respectfully, the examiner disagrees.
Surya et al (US 10,713,821) states at Col 8 ln 24-31 “To obtain a discriminative representation for color, coarse clusters of perceptually similar colors may be derived (and/or selected) in a color dataset that may be mapped to text descriptions referencing a particular color attribute. In various examples, tagged color attributes for objects (e.g., data representations of objects of interest) in any desired dataset that includes color tags may be used to generate the color clusters.” In the color modeling of the GAN, the color inputs (understood as user selection) are provided and the synthetic images are further enhanced (further image analysis) towards the user specification. The selection by a user is supported in the description by Surya et al at Col 10 ln 3-8 regarding the architecture 300 of the input devices 308 that include a display component such that “When the display component 306 includes a touch-sensitive display, the input devices 308 can include a touch sensor that operates in conjunction with the display component 306 to permit users to interact with the image displayed by the display component 306 using touch inputs (e.g., with a finger or stylus).”
The applicant’s claim limitation is broad for the selected synthetic image to be subject to “image analysis.” There is no context in the claim as to what “image analysis” is performed. The interpretation could just indicate as simple as the processing system recognition of a given selection with no further processing. The specification broadly describes the concept as well (specification ¶ [0008], [0012]). No drawings were provided to readily resolve the broad interpretation.
Applicant has acknowledged a selection of a color is performed in the prior art (Remarks – 09/16/2026, pg 7-8) and argues the selection is associated with text input from a user and not selection of an image. Although the examiner interprets the selection performed in the prior art may be performed using the touch-screen display, the claim has no limitations as to how selection is performed and could be made based on other forms of user input as contemplated by Surya et al at Col 9 ln 51-67:
The architecture 300 may also include one or more input devices 308 operable to receive inputs from a user. The input devices 308 can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, trackball, keypad, light gun, game controller, or any other such device or element whereby a user can provide inputs to the architecture 300. These input devices 308 may be incorporated into the architecture 300 or operably coupled to the architecture 300 via wired or wireless interface. In some examples, architecture 300 may include a microphone 370 for capturing sounds, such as voice commands. Voice recognition engine 380 may interpret audio signals of sound captured by microphone 370. In some examples, voice recognition engine 380 may listen for a “wake word” to be received by microphone 370. Upon receipt of the wake word, voice recognition engine 380 may stream audio to a voice recognition server for analysis.
Respectfully, the examiner is not persuaded.
Applicant argues the prior art does not teach “generating a plurality of image representations of a fictitious non-existing objects with aid of linguistic specifications” in independent claim 22 (Remarks – 09/16/2026, pg 8). Respectfully, the examiner disagrees.
As discussed above, Surya et al contemplates numerous types of input communications from a user, including verbal commands from a user at Col 9 ln 59-67. There are no details provided by the applicant in the specification as to unique features of the linguistic input (specification ¶ [0008]) and the claim limitation broadly claims “aid of linguistic specifications” without further detail rendering the interpretation as an input from the user in terms of an audio signal, parallel to the disclosure of the prior art Surya et al.
Respectfully, the examiner is not persuaded.
No further argument is presented.
All arguments were addressed.
Claim Objections
Applicant is advised that should claim 1 be found allowable, claim 22 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Applicant is advised that should claim 17 be found allowable, claim 15 with base claim 1 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112(a) – written description
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 5-9, 13-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 1, 17, and 22 recite at least the following limitations lacking written description:
“an image generation program of an electronic data processing system with Artificial Intelligence”; and “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”.
Each of these limitations fail to comply with the written description requirement. The claims each contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 5-9, 13-16 are rejected as dependent on claim 1.
Claims 18-21 are rejected as dependent on claim 17.
The written description standard under 35 U.S.C. § 112(a) requires that the “specification shall contain a written description of the invention”. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. An applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). Possession may be shown in a variety of ways including description of an actual reduction to practice, or by showing that the invention was “ready for patenting” such as by the disclosure of drawings or structural chemical formulas that show that the invention was complete, or by describing distinguishing identifying characteristics sufficient to show that the applicant was in possession of the claimed invention. See, e.g., Pfaff v. Wells Elecs., Inc., 525 U.S. 55, 68, 119 S.Ct. 304, 312, 48 USPQ2d 1641, 1647 (1998); Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406; Amgen, Inc. v. Chugai Pharm., 927 F.2d 1200, 1206, 18 USPQ2d 1016, 1021 (Fed. Cir. 1991).
There is a presumption that an adequate written description of the claimed invention is present when the application is filed. In re Wertheim, 541 F.2d 257, 263, 191 USPQ 90, 97 (CCPA 1976) (“we are of the opinion that the PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims”).
However, as discussed in subsection I., supra, the issue of a lack of adequate written description may arise even for an original claim when an aspect of the claimed invention has not been described with sufficient particularity such that one skilled in the art would recognize that the applicant had possession of the claimed invention. The claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art (emphasis). While it is not necessary for the examiner to present factual evidence, to make a prima facie case it is necessary to point out the claim limitations that are not adequately supported and explain any other reasons that the claim is not fully supported by the disclosure to show that the inventor had possession of the invention. See for example, Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007).
The courts have described the essential question to be addressed in a description requirement issue in a variety of ways. An objective standard for determining compliance with the written description requirement is, “does the description clearly allow persons of ordinary skill in the art to recognize that he or she invented what is claimed.” In re Gosteli, 872 F.2d 1008, 1012, 10 USPQ2d 1614, 1618 (Fed. Cir. 1989). Under Vas-Cath, Inc.v. Mahurkar, 935 F.2d 1555, 1563-64, 19 USPQ2d 1111, 1117 (Fed. Cir. 1991), to satisfy the written description requirement, an applicant must convey with reasonable clarity to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention, and that the invention, in that context, is whatever is now claimed. The test for sufficiency of support in a parent application is whether the disclosure of the application relied upon “reasonably conveys to the artisan that the inventor had possession at that time of the later claimed subject matter.” Ralston Purina Co.v.Far-Mar-Co., Inc., 772 F.2d 1570, 1575, 227 USPQ 177, 179 (Fed. Cir. 1985) (quoting In re Kaslow, 707 F.2d 1366, 1375, 217 USPQ 1089, 1096 (Fed. Cir. 1983)). See MPEP§ 2163.
Regarding Claims 1, 17, 22, with dependent claims 5-9, 13-16, and 18-21, the applicant claims multiple limitations that, upon careful review of applicant’s R.C.E. and associated arguments, the examiner has thoughtfully determined the applicant’s specification does not meet the minimum threshold to satisfy the written description for, at least, the following limitations:
“an image generation program of an electronic data processing system with Artificial Intelligence” – no details were provided regarding the image generation program with Artificial Intelligence. The specification does not disclose any details regarding the program or the artificial intelligence system and the specification even states the generated representations may be of a “mental display” (specification ¶ [0023]), indicating the machine could be purely one’s own thoughts. The notion that the generation of “fictitious non-existing objects” generated in the mind is further expanded as the applicant’s drawing demonstrate a person with an idea of an object (element 12 of Figure 1) and the AI electronic processing system 14 shown as a black box (element 14 of Figure 1). The applicant provides no details regarding the artificial intelligence system regarding how it generates an image. There are no meaningful limits presented by the applicant in how the program is organized, how it processes input data or how it generates images. The examiner, upon further review of the applicant’s disclosure is of the opinion the applicant did not meaningfully have possession of the invention at the time of filing using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention.
“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” – no details were provided regarding the image analysis. The specification does not disclose any details regarding what the “image analysis” is performed by the processing system but rather states it is performed and “using the result of the image analysis to search for identical or similar real products in the network” (specification ¶ [0008]). The specification therefore indicates an analysis happens and from the analysis a further search is performed. The actual “image analysis” is not disclosed. It would be pure speculation as to interpret what type of image analysis is performed. The analysis could be as simple as recognition that an image was present, could include various types of pre-processing, further comparative analysis of data within an image, or matching the image to other metadata. There are no meaningful limits in the interpretation of what “image analysis” is performed by “subjecting the generated representation to an image analysis.” There are no meaningful limits presented by the applicant in how the analysis is organized, how it processes input data or how it analyzes images. The examiner, upon further review of the applicant’s disclosure is of the opinion the applicant did not meaningfully have possession of the invention at the time of filing using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention.
Each of these limitations fail to comply with the written description requirement. The claims each contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Therefore, Claims 1, 5-9, 13-22 are rejected under 35 U.S.C. 112(a) for lack of written description.
Claim Rejections - 35 USC § 112(b)
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.
Claims 15, 1 are 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.
Claims 15, 17 recites the limitation “selecting the at least one identical or similar real product found by the searching with an operating pointer of the electronic data processing system” (reciting claim 17 and claimed in parallel for claim 15). After careful review, it is unclear how to interpret the limitation as it is not clear if the “selecting” is performed by a user or the processing system as the independent claims appear to incorporate perspectives of both the user action and the processing system. Thus, Applicant has failed to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 18-21 are rejected based on their dependency to claim 17.
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, 19-22 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), the method comprising:
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),
wherein each of the generated plurality of image representations contains 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 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 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 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 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 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 selected with an operating pointer thereby obtaining 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 a predetermined 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), the method comprising:
generating with an electronic data processing system 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) based on user-generated linguistic or textual specifications input into an artificial intelligence image generation program (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),
each of the generated plurality of image representations each contain all of the specifications (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 (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 with the electronic data processing system at least one of the generated plurality of image representations selected by a user to an image analysis (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; displaying on the display device of the electronic data processing system at least one identical or similar real product found as a result of the searching; and selecting the at least one identical or similar real product found by the searching is selectable with an operating pointer of the electronic data processing system thereby obtaining further information about the at least one identical or similar real product.
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]);
displaying on the display device of the electronic data processing system at least one identical or similar real product found as a result of the searching (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]); and
selecting the at least one identical or similar real product found by the searching is selectable with an operating pointer of the electronic data processing system thereby obtaining further information about the at least one identical or similar real product (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]), upon which the user may click (understood to be performed with a computer mouse) on a source document of an image to obtain additional information regarding the item selected; ¶ [0049]).
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; displaying on the display device of the electronic data processing system at least one identical or similar real product found as a result of the searching; and selecting the at least one identical or similar real product found by the searching is selectable with an operating pointer of the electronic data processing system thereby obtaining further information about the at least one identical or similar real product. 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 predetermined 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 22, 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), the method comprising:
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 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),
wherein each of the generated plurality of image representations contains all 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 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 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 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 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]).
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 fictitious non-existing objects are selected and at least one new pictorial representation of a fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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 fictitious non-existing objects are selected (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 fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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 fictitious non-existing objects are selected and at least one new pictorial representation of a fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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 fictitious 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 fictitious 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 selected with an operating pointer thereby obtaining 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 predetermined 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 fictitious non-existing objects are selected and at least one new pictorial representation of a fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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 fictitious non-existing objects are selected (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 fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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 fictitious non-existing objects are selected and at least one new pictorial representation of a fictitious non-existing object is generated from the selected generated plurality of image representations of fictitious 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.
Barnes et al (US 2019/0026394) teach a method and system for generating images of articles of manufacture based on user input.
Larson (US 2014/0143082) teach a method and system for generating customized retail designs based on input from a user.
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 products for a customer to evaluate the product.
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.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
