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 .
Claims Status
Claims 6, 13-20 and 27 have been cancelled.
Claims 29-30 are newly added.
Claims 1-5, 7-12, 21-26 and 28-30 are pending and stand rejected.
Interview Summary
The Examiner contacted Applicant to discuss proposed amendments; however, agreement was not reached on the proposal dated August 17, 2026 (see Interview Appendix attached to this office action).
Notably, subsequent art has been found and applied below that now renders that proposal moot.
Response to Arguments
Applicant’s arguments made with respect to the rejection under 35 USC 101 have been fully considered but are not persuasive.
Applicant initially argues that the Examiner’s determination under Prong One is incorrect because it oversimplifies the analysis. The Examiner disagrees, and notes that the courts have consistently summarized the abstract limitations when considering limitations individually and as a whole.
Additionally, Applicant is reminded Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Both the MPEP and the courts have declined to define abstract ideas, other than by example. Both the office and the courts have provided consistent guidance instructing examiners to refer to the body of case law precedent in order to identify abstract ideas by way of comparison to concepts already found to be abstract.
Applicant further argues that “the techniques described in claim 1 address technical problems inherent to visual search systems, such as bridging the semantic gap between visual queries and textual item descriptions, extracting user intent from images rather than relying on visual similarity matching, and enabling refinement of automatically generated search parameters through a specialized interface” (e.g., p. 13). Applicant is again reminded that analysis of potential technical improvements occurs in Prong Two, and forms part of the Step 2A “directed to” inquiry (examiners “then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception).
The Examiner maintains that the claims clearly set forth and describe abstract ideas. The Examiner’s analysis is consistent with that demonstrated by the courts and consistent with the guidance of the MPEP, providing a clear articulation of those elements that set forth and describe the abstract idea(s).
Turning to Prong Two, the alleged “bridging the gap” does not stem from any underlying improvement in technology. The crux of the invention is in the mere use of generic image analysis and machine learning techniques to automate the analysis of an object. Simply because this is applied within an interface using generic interface elements (e.g., buttons or checkboxes) does not move this invention from ineligible to eligible. The Examiner draws further attention to Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), which found that arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly. Like Trading Technologies, the claimed invention does not offer an improvement to the interface itself. Instead, the interface is merely the mechanism through which the search is conducted.
Applicant has not invented a new interface, nor has applicant invented new interactive elements. Instead, generic interface elements (such as check boxes or drop downs) may be used to perform edits to a search. As with most of the disclosure and claims, this application offers very little if any disclosure with respect to how the interface may be generated, how the population of search terms is accomplished, or how any of the underlying technical functioning is performed in a manner that would illustrate to one of ordinary skill in the art that the claimed invention is more than merely applying the abstract idea of using an image to perform a product search – i.e., the claims are nothing more than “apply it". Simply stating what information is sent to or displayed by the interface is not tantamount to its underlying functionality.
While Applicant argues that “conventional techniques are limited to searching for images that are visually similar to the input image”, this itself is abstract. A visual search based on similarity is readably performable by a human and defines part of the commercial activity itself. The claims do not reflect specific improvements in image analysis, employing only generic techniques for analyzing the image to determine characteristics of an object in the image and extract visual features from the image that represent the characteristics of the object, at least a portion of the images of the items having injected noise (which form part of the abstract idea).
Applicant also expressly states that the result is “enabling more accurate and intent-driven search results” under the guise of improving technology. Such an improvement is not an improvement to the functioning of the computer itself or another technology or technical field, including the interface. This is an abstract-idea based improvement to the abstract idea itself, and the utilization of machine learning is nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, mere use a computer as a tool to perform an abstract idea). Moreover, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform an abstract idea with greater speed and efficiency than could previously be achieved.
Similar analysis as applied under Prong Two is also applied to the arguments under Step 2B. Simply put: there is no “inventive concept” provided by the additional elements of claims, taken individually or as a whole. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer, no more than a general link to a technological environment. They also operate using only well-understood, routine and conventional computer operations, such as receiving or transmitting data over a network, performing repetitive calculations, and presenting offers (see MPEP 2106.05(d)(II)). Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
With respect to receiving, by a computing device, a search query for items listed on an online marketplace, the search query including an image captured by activating a camera associated with the computing device, the only portions of this limitation that are not abstract include by a computing device and an online marketplace. The remaining portions receiving a search query for items listed on an [[online]] marketplace, the search query including an image captured by activating a camera associated with the computing device form part of the abstract idea. The phrasing the search query including an image captured by activating a camera associated with the computing device is nothing more than a description of how the image was obtained. This is a past-tense, passive description of the information that is received and fails to set forth any active activation or capturing of the image.
The core functions performed by the computer above is literally receiving or transmitting data, which is an enumerated well-understood, routine and conventional function (see: MPEP 2106.05(d)(II)(i)). The online marketplace is nothing more than a general link to a networked environment.
Similar logic is applied by Applicant to other limitations where Applicant conflates what is abstract with what is additional. Here again, limitations such as displaying and populating are nothing more than mere instructions to implement the abstract idea for which the underlying computer operations correspond to routine functions such as transmitting data and/or presenting offers.
Ultimately, under either Prong Two or Step 2B, the claims are nothing more than applying the abstract idea of searching for items in a marketplace using an image through the use of generic computing technology and generally linking the abstract idea to a particular technological environment. Accordingly, the rejection is maintained.
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 1-5, 7-12, 21-26 and 28-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-5, 7-12, 21-26 and 28-30, under Step 2A claims 1-5, 7-12, 21-26 and 28-30 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites
a method comprising:
receiving a search query for items listed on an online marketplace, the search query including an image captured by activating a camera associated with the computing device;
analyzing the image to determine characteristics of an object in the image,
extract visual features from the image that represent the characteristics of the object, at least a portion of the images of the items having injected noise;
automatically generating one or more search terms, including a type of the object, based on the characteristics of the object in the image and populating the one or more search terms;
receive an edit to the one or more search terms, including an edit to the type of the object;
locate items matching the one or more search terms, in response to receiving the edit; and
displaying visual indications of the items matching the one or more search terms in the online marketplace.
These limitations recite ‘certain methods of organizing human activity’, such as by performing commercial interactions (see: MPEP 2106.04(a)(2)(II)). This is because claim 1 recites searching for items in a marketplace using an image. This represents the performance of marketing or sales activities or behaviors, which is a commercial interaction and falls under organizing human activity.
Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas.
Additionally, claim 1 can also be understood to recite limitations that set forth or describe “mental processes” that are performable in the human mind, or by pen and paper. This is because at least the following limitations can be accomplished in the human mind, or using a physical aid such as pen and paper, and represent observations, evaluations or judgments (see: MPEP 2106.04(a)(2)(III)):
analyzing the image to determine characteristics of an object in the image,
extract visual features from the image that represent the characteristics of the object, at least a portion of the images of the items having injected noise.
Notably, the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.
Accordingly, under step 2A (prong 1) claim 1 also recites an abstract idea because claim 1 recites limitations that fall within the “Mental processes” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including:
a computer-implemented method,
an online marketplace,
using a machine learning model trained using images of items listed on the online marketplace,
a search interface,
transmitting, by the computing device over a network, the one or more search terms to one or more servers of the online marketplace
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). This is true even with respect to using a machine learning model trained using images of items listed on the online marketplace, which merely leverages this additional element to perform the abstract idea of analyzing images and determining characteristics. That is, each of the additional elements (including the use of the machine learning model), taken alone or in combination, represents nothing more than applying the abstract idea using generic computing components such that they merely use a computer as a tool to perform an abstract idea.
Further, the additional elements (e.g., “online” marketplace) do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer, or no more than a general link to a technological environment.
The additional elements also operate using well-understood, routine and conventional computer operations, such as receiving or transmitting data over a network and presenting offers (see MPEP 2106.05(d)(II)). Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-5, 7-9 and 25-26, dependent claims 2-5, 7-9 and 25-26 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1. As such, claims 2-5, 7-9 and 25-26 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-5, 7-9 and 25-26 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, claims 2-9 rely upon at least elements as recited in claim 1. Further additional elements (e.g., checkboxes of claim 25, dropdown menu of claim 26) are also recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
With specific reference to claims 5-6 and 27, which recite aspects related to machine learning, the Examiner maintains that the claims do not integrate the abstract idea into a practical application. Claim 5 recites that the machine learning model is trained using training data, while claim 6 recites “adding noise”. Claim 6 only adds noise to the data without performing any subsequent training, as does claim 27. As written, it is not inherent that the model is iteratively or even actively trained, and none of these claims reflect any form of active training by adding/injecting noise (e.g., after an initial training, or during an active training phase). Moreover, paragraph 0087, however, describes using known types of noise (e.g., Gaussian Noise, Poisson Noise) that achieve known improvements as an ancillary part of performing the abstract idea.
Even assuming arguendo the paragraphs do demonstrate an inventive improvement by Applicant (which the Examiner does not acquiesce), the claims as written do not achieve this improvement because they do not positively recite training using the noise data (which is required to achieve the improvement), and these elements simply extend the mere use of a [previously] trained machine learning model (as discussed with respect to claim 1). Accordingly, claims 2-5, 7-9 and 25-26 do not integrate the recited exception into a practical application.
Lastly, under step 2B, claims 2-5, 7-9 and 25-26 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware and/or generally link the exception to a technological environment. Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 2-5, 7-9 and 25-26 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 10-12 and 28-29 (system) and claims 21-24 and 30 (non-transitory CRSM), claims 10-13 and 28-29 and claims 21-24 and 30 recite at least substantially similar concepts and elements as recited in claims 1-5, 7-9 and 25-26 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Further additional elements such as memory, storage media, instructions, et al. similarly represent mere use of generic computing components to implement the abstract idea. As such, claims 10-16, 28-29, 21-24 and 30 are rejected under at least similar rationale.
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.
Claim(s) 1-4, 9-11, and 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao (US 9,875,258) in view of Lee (2017/0256038).
Regarding claim 1, Hsiao discloses a computer-implemented method comprising:
receiving, by a computing device, a search query for items listed on an online marketplace, the search query including an image captured by activating a camera associated with the computing device (see: Fig. 1A-1B, col. 2 lines 37-61);
analyzing the image to determine characteristics of an object in the image by the computing device using a machine learning model trained using images of items listed on the online marketplace to extract visual features from the image that represent the characteristics of the object (see: col. 1 line 65-col. 2 line 2, col. 2 lines 8-16, col. 9 lines 46-66, Fig. 4 (402), Fig. 5 (502-504)),
automatically generating one or more search terms, including a type of the object (e.g., category, Fig. 1E (162)), based on the characteristics of the object in the image (see: col. 2 lines 11-18, col. 7 lines 40-44, Fig. 5 (522)) and populating the one or more search terms into a search interface, by the computing device (see: Fig. 1E-1F (160-190), col. 6 lines 55-62);
displaying the search interface on a display associated with the computing device, the search interface including one or more interactive elements corresponding to each of the one or more search terms and configured to receive an edit to the one or more search terms, including an edit to the type of the object (see: Fig. 1E-1F, col. 2 lines 18-24, col. 10 lines 27-32, Fig. 5 (530));
transmitting, by the computing device over a network, the one or more search terms to one or more servers of the online marketplace to locate items matching the one or more search terms, in response to receiving the edit (see: col. 2 lines 21-24, col. 6 lines 20-26, col. 9 lines 1-6, col. 10 lines 24-26, col. 12 lines 18-34, Fig. 1E (192, 194)[Wingdings font/0xE0]1F (196, 198), Fig. 3 (312), Fig. 5 (524-530), Fig. 8 (806, 808)) and
displaying, in the search interface, visual indications of the items matching the one or more search terms in the online marketplace (see: col. 6 line 22-26, col. 10 lines 24-32, Fig. 1E-1F (192-198), Fig. 5 (526, 530)).
Though disclosing all of the above, Hsiao does not teach that the machine learning model was trained on data including where at least a portion of the images of the items having injected noise. The use of such techniques (e.g., added noise) in the realm of machine learning was notoriously well-known before the effective filing date of the invention, and would have been obvious.
For example, Lee teaches a method for image analysis (e.g., abstract, 0028) that applies the known technique of training a neural network by adding noise to images in the training data (see: 0031, 0039, 0047, 0057). Accordingly, Lee discloses a machine learning model that, when implemented, was trained on data including at least a portion of the images having injected noise.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Hsiao to have utilized the known technique for adding noise to image training data as taught by Lee in order to have prevented overfitting certain training images and thereby allowing the model Hsiao to have become more robust against noise (see: Lee: 0031, 0047).
2. The computer-implemented method of claim 1, further comprising displaying the one or more search terms proximate to the visual indications of the located items in a user interface (see: Hsiao: Fig. 1E-1F).
Note: Elements 160-190 re displayed above and “proximate” too results 192-194. At best, however, this represents the mere arrangement of parts and does not distinguish the claim from the prior art. See MPEP 2144.04(VI)(C).
3. The computer-implemented method of claim 1, further comprising: receiving a user input to remove at least one of the one or more search terms and responsive to the user input to remove the at least one of the one or more search terms, filtering the visual indications of the located items (see: col. 10 lines 29-32, col. 6 lines 52-66, col. 7 lines 3-7, Fig. 1E-Fig. 1F, Fig. 5 (530)).
4. The computer-implemented method of claim 1, wherein the characteristics are based on intended characteristics of an item to purchase (see: Hsiao: col. 1 line 65-col. 2 line 16, col. 7 lines 32-44).
9. The computer-implemented method of claim 1, wherein the characteristics of the object designate a price, a brand, or a designation of luxury for the object in the image (see: Hsiao: Fig. 1E-1F (164, 166), col. 6 lines 8-19).
Note: In addition to the teachings of Hsiao, merely labeling the characteristics of the items differently from the prior art fails to impart a new and nonobvious functioning of the method step. The subjective labeling of the characteristics represents non-functional descriptive material and does not patentably distinguish the claimed invention from the prior art.
Regarding claims 10-11, claims 10-11 recite at least substantially similar concepts and elements as recited in at least claims 1-2 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 10-11 are rejected under at least similar rationale.
Regarding claims 21-24, claims 21-24 recite at least substantially similar concepts and elements as recited in at least claims 1-4 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 21-24 are rejected under at least similar rationale.
Claim(s) 5, 8 and 12 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Lee as applied to claims 1 and 10 above, and further in view of Kale (US 2017/0193011).
Regarding claim 5, Hsiao in view of Lee including applying a machine learning techniques to an image search and training the machine learning model used in image searching (see again: Hsiao). Notably, Hsiao also teaches descriptive data for the images – e.g., wherein the images of the items listed on the online marketplace are associated with characteristics of the items, the characteristics of the items extracted from descriptive data (see: col. 5 lines 56-66, col. 6 lines 5-7, col. 8 lines 3-7, col. 9 lines 48-50). Hsiao, however, does not teach that the characteristics of the items are extracted from listing data. One of ordinary skill in the art, however, would have readily understood that the need for “training” machine learning models was well-established.
To this accord, Kale teaches a query system for item listings that utilizes one or more machine learning models that are trained using training data that includes images of items listed on the online marketplace (see: 0018, 0039, 0044, Fig. 5 (510, 530), Fig. 6 (610, 620), Fig. 3 (330-360)), the images of the items associated with characteristics of the items, the characteristics of the items extracted from listing data (see: 0019, 0030, 0038-0039, 0044, Fig. 4 (420, 430), Fig. 5 (510, 520)). In Kale, images withing the listings are fed to the trained image classifiers to identify attributes (i.e., extract characteristics) of the depicted items within the listings. The data may also include text information from the item listing. In each case, the information being analyzed includes listing data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Hsiao in view of Lee to have utilized the known technique for training a machine learning model using a training image set as taught by Kale in order to have enabled the machine learning techniques of Hsiao in view of Lee to have better identified attributes in items and improved the quality of search results while reducing the effort in searching for items (see: Kale: 0056-0057).
8. The computer-implemented method of claim 1, wherein the one or more machine learning models are trained using user purchase history (see: Kale: 0019 (interacted with listings), 0036-0037, 0043, Fig. 4 (410)
Regarding claim 12, claim 12 recites substantially similar limitations and scope as recited in claim 5. As such, claim 12 is rejected under at least similar rationale.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Lee as applied to claims 1 and 10 above, and further in view of Badjatiya (US 2022/0237406).
Regarding claim 7, Hsiao in view of Lee teaches all of the above as noted but does not teach wherein the machine learning model is trained using training data that includes images uploaded to the online marketplace as part of a search query
To this accord, Badjatiya teaches an image search system that employs a trained machine learning model (see: 0058-0059, 0085, Fig. 7), wherein the machine learning model is trained using training data that includes images uploaded to the online marketplace as part of a search query (see: 0021, 0059 (source image), Fig. 6 (210), Fig. 7 (720), Fig. 9 (910)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Hsiao in view of Lee to have utilized the known technique for employing trained machine learning models as taught by Badjatiya in order to have enabled the invention of Hsiao in view of Lee to have generated target features which were more accurately tailored to user desires and which in turn provided improved image search results (see: Badjatiya: 0017).
Claim(s) 25, 28 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Lee as applied to claim 1 above, and further in view of Ouyang (US 2019/0213608).
Regarding claim 25, Hsiao in view of Lee teaches all of the above as noted including search refinement and editing through selectable elements(see again: Hsiao) but does not teach wherein the one or more user interface includes checkboxes indicating different types of objects for receiving the edit to the one or more search terms.
Such techniques were well-established before the effective filing date of the invention and would have been obvious.
For example, Ouyang teaches a search refinement interface that comprises checkboxes indicating different options for receiving the edit to the one or more search terms (see: Fig. 6A (610B-F), 0059). Notably, Ouyang discloses a category (i.e., object type) selector (see: Fig. 6 (610A), 0059).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Hsiao in view of Lee to have utilized the well-known technique for search refinement as taught by Ouyang in order to have provided search options that enabled a user to more easily and accurately refine, limit or invoke a subset of results (see: Ouyang: 0059).
Regarding claims 28 and 30, claim 28 and 30 recite substantially similar limitations and scope as recited in claim 25. As such, claim 28 and 30 are rejected under at least similar rationale.
Claim(s) 26 and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Lee as applied to claim 1 above, and further in view of Bagley (US 10,997,601).
Regarding claim 25, Hsiao in view of Lee teaches all of the above as noted including search refinement and editing through selectable elements (see again: Hsiao) but does not teach wherein the one or more user interface includes a drop-down menu indicating different types of objects for receiving the edit to the one or more search terms.
Such techniques were well-established before the effective filing date of the invention and would have been obvious.
For example, Bagley teaches a search refinement interface that comprises a drop-down menu indicating different options for receiving the edit to the one or more search terms (see: Fig. 5 (504), col. 10 lines 53-57, Fig. 4 (404), col. 10 lines 41-44). Notably, Bagley discloses the filter corresponds to a type of product (e.g., shirt, short, pants, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Hsiao in view of Lee to have utilized the well-known technique for search refinement as taught by Bagley in order to have provided search filters that enabled a user to more specifically refine a search (see: Bagley: col. 10 lines 53-57).
Regarding claim 29, claim 29 recites substantially similar limitations and scope as recited in claim 26. As such, claim 29 is rejected under at least similar rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
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WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/ Primary Examiner, Art Unit 3619
Prosecution Insights