DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 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.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 37-40 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 17-20 of prior U.S. Patent No. 12282946. This is a statutory double patenting rejection.
The claims are not identical, however they are directed to identical inventions because merely leaving out the recitation of “a product recommendation system” in limitations such as “receiving, by a product recommendation system from a user device” does not change the subject matter of the claims because they are clearly still directed to the product recommendation system that receives from the user device in this exemplary step. Similarly, leaving the word “product” out of the recitation “anchor product” does not change the subject matter claimed because “anchor” still clearly refers to an anchor product. This leaves the last change in recitation, changing “product” to “item.” This is also not a substantive change because the claim is still directed to exactly the same subject matter regardless of whether the element is referred to as a product or item.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-36 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12282946.
Although the claims at issue are not identical, they are not patentably distinct from each other because every limitation presently claimed is also claimed in the parent patent. The parent includes additional limitations, e.g.,
“A computing device, comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the computing device to perform a method, comprising: receiving, from a user device, a selection of an anchor; generating text embeddings comprising vector descriptions of text associated with each item of a plurality of items in a database for each item of the plurality of items, wherein the text embeddings are generated by a machine learning model trained with item information for the plurality of items in the database, and the machine learning model comprises a multi-stream network; generating similarity scores between the anchor and each item in the database based on a text embedding and image feature of the anchor and a text embedding and image feature of each item; weighting the similarity scores based on an item type of the anchor, wherein the item type is based on the text embedding of the anchor; selecting at least one of the items from the database having a highest weighted score; and causing the user device to display on a display of the user device the at least one of the items as selected, responsive to the selection of the anchor.” Claim 21, inter alia, present application.
Compared with
“the product recommendation system comprises: a processor; and a memory storing instructions that, when executed by the processor, cause the system to perform a method, comprising: receiving, by the product recommendation system from a user device, a selection of an anchor product; training, by the processor, a machine learning model with product information for a plurality of products in a product database; generating, from the machine learning model, text embeddings comprising vector descriptions of text associated with each product of the plurality of products in the product database for each product of the plurality of products, wherein the machine learning model comprises a multi-stream network; calculating, by the product recommendation system, a similarity score between text embeddings of the anchor product and text embeddings of a plurality of products in a product database; calculating, by the product recommendation system, a similarity score between an image feature of the anchor product and an image feature of the plurality of products in the product database, wherein the image features comprise vector descriptions of color content of the images; calculating, by the product recommendation system, a weighted score between the two similarity scores as calculated for the anchor product and the plurality of products in the product database, wherein a weight of the similarity score between the image feature of the plurality of products in the product database and a weight of the similarity score between text embeddings of the anchor product and text embeddings of the plurality of products in the product database is varied using a product type determined from the text embeddings of the anchor product; selecting, by the product recommendation system, at least one of the products from the product database having a highest weighted score; and returning, to the user device by the product recommendation system, the at least one of the products as selected responsive to the selection of the anchor product, automatically causing the user device to display on a display of the user device the at least one of the products as selected responsive to the selection of the anchor product.” Claim 1, U.S. Patent No. 12282946.
As can be seen from the above comparison all of the features in bold from the parent are included in the present claim. Once slight variations in the wording of certain limitations is accounted for, (such as substituting “product” with “item”), it can be seen that the present claim includes no limitations that are not also claimed in the parent. The analysis of patented independent claim 9 and present independent claim 29 is essentially the same, and the dependent claims of each are virtually identical.
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 21-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 21-28 are directed to a machine and claims 29-36 are directed to a process. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving, from a user…, a selection of an anchor; generating text embeddings comprising vector descriptions of text associated with each item of a plurality of items in a database for each item of the plurality of items, wherein the text embeddings are generated by a machine learning model trained with item information for a plurality of items in the database, and the machine learning model comprises a multi-stream network; generating similarity scores between the anchor and each item in the database based on a text embedding and image feature of the anchor and a text embedding and image feature of each item, wherein the text embeddings of each item comprise vector descriptions of text associated with each item; weighting the similarity scores based on an item type of the anchor, wherein the item type is based on the text embedding of the anchor; selecting at least one of the items from the database having a highest weighted score; and causing the user device to display … the at least one of the items as selected, responsive to the selection of the anchor.” Claim 29.
With recited additional elements reserved for consideration alone and all together combined with their recited roles in the claim under step 2A prong two, a careful analysis of the remaining limitations above results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as mathematical concepts, such as mathematical relationships, mathematical formulas or equations, and mathematical calculations, and certain methods of organizing human activity, for example commercial interactions (including advertising, marketing or sales activities or behaviors). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in the present claims are a user device and a display of the user device. Claims 21-28 also include a processor and a memory storing instructions for the processor. These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the user device and its display only serve as a stand-in node for the user, a source of input and a recipient of output. The processor and memory of claims 21-29 are simply recited broadly and generally recited as implementing all steps in terms of the intended results of functionally nonspecific activities. These additional elements do not integrate the judicial exception into a practical application because they amount to no more than mere instructions to apply the exception using generic computer components. Based on the description, the “multi-stream network” is not an additional element because it is merely an abstract characterization of a neural network. It does not refer to any system components or architecture. The claim is almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of the steps without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). Based on these considerations, there are no particular machines or manufactures identified in the present claims.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The claims fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that the dependent claims 22-28 and 29-36 do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas further describing the data and manipulations of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 21-36 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-24, 28-31, and 36, are rejected under 35 U.S.C. 103 as being unpatentable over Joshi et al. (Pub. No. US 2018/0032882 A1) in view of Zhang et al. (Nonpatent literature identified as item V on attached form PTO-892).
Joshi teaches, a) generating text embeddings comprising vector descriptions of text, b) vector descriptions of text associated with each item of a plurality of items in a database, c) wherein the text embeddings are generated by a machine learning model, and d) a machine learning model trained with item information for the plurality of items in the database, and discloses regarding
Claim 21. A computing device, comprising: ● a processor (see at least Joshi fig. 7, ¶0006); and ● a memory storing instructions that, when executed by the processor, cause the computing device to perform a method (see at least Joshi ¶¶0005, 0054), comprising: ● receiving, from a user device, a selection of an anchor (see at least Joshi abstract "visual content ... posted to a social media platform," ¶0034 "historical selections by the user");
● generating similarity scores between the anchor and each item in the database based on a text embedding and image feature of the anchor and a text embedding and image feature of each item (see at least Joshi abstract “extracting concept information from visual content," figs. 1 "extracted metadata," 4, 7, ¶¶0004, 0025, ¶0028 "extracted metadata may be encoded as textual characteristics," 0030 "cosine similarity may be used as a method for computing similarity"); ● weighting the similarity scores based on an item type of the anchor, wherein the item type is based on the text embedding of the anchor (see at least Joshi ¶¶0020-0021 describe product concepts and classifiers, ¶0027 "category information associated with them. This information may also be incorporated into the extracted metadata," ¶0037. Please note: Examiner's position is that product type is defined by concepts and classifiers.); ● selecting at least one of the items from the database having a highest weighted score (see at least Joshi ¶0032 "items receiving a high rank may be recommended"); and ● causing the user device to display on a display of the user device the at least one of the items as selected, responsive to the selection of the anchor (see at least Joshi abstract, figs. 1, 7, ¶0002 "provide a user with recommendations").
Joshi teaches all of the above as noted. Joshi discloses a) generating text embeddings comprising vector descriptions of text, b) vector descriptions of text associated with each item of a plurality of items in a database, c) wherein the text embeddings are generated by a machine learning model, and d) a machine learning model trained with item information for the plurality of items in the database, but does not explicitly disclose that the machine learning model comprises a multi-stream network.
Zhang also teaches a) generating text embeddings comprising vector descriptions of text, b) vector descriptions of text associated with each item of a plurality of items in a database, c) wherein the text embeddings are generated by a machine learning model, and d) a machine learning model trained with item information for the plurality of items in the database, and further discloses that the machine learning model comprises a multi-stream network, wherein the method further comprises:
● generating text embeddings comprising vector descriptions of text associated with each item of a plurality of items in a database for each item of the plurality of items, wherein the text embeddings are generated by a machine learning model trained with item information for the plurality of items in the database, and the machine learning model comprises a multi-stream network (see at least Zhang §3 fig. 2 describing the ENCORE framework, wherein the ENCORE architecture comprises a multi-stream network, §3.1 trains a text embedding model using a distributed representation on product titles and descriptions to generate fixed-length text feature vectors for each product (the text feature vector t_i for item l_i includes the title and description), §3.1 trains a separate image embedding model using CNN features pre-trained on lmageNet (4096-dimensional image feature vectors), §3.3 one stream processes image inputs through an image embedding to compute image distance, and a separate parallel stream processes text inputs through a text embedding to compute text distance. These two streams are concatenated into a merged multi-modal layer (Equations in Section 3.3) before passing through non-linear neural layers, §4.1, a neural complementary item recommendation system that trains on large product datasets (over 1 million products across six Amazon product categories). This two-stream architecture, with separate parallel processing of image and text modalities followed by concatenation, constitutes a multi-stream network as recited.).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Joshi to include that the machine learning model comprises a multi-stream network, as taught by Zhang since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Zhang in the method of Joshi. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Joshi in view of Zhang teaches all of the above as noted and Joshi further discloses, regarding
Claim 22. The computing device of claim 21, wherein each weighted similarity score includes a text weight value based on an item type and an image weight value based on the item type (see at least Joshi ¶¶0020-0021 describe product concepts and classifiers extracted from images, ¶0027 "category information associated with them. This information may also be incorporated into the extracted metadata," ¶0037. Please note: Examiner's position is that product type is defined by concepts and classifiers identifiable in an image.).Claim 23. The computing device of claim 22, wherein the item type is one of a first item type and a second item type, the first item type having a greater importance of visual features and a lower importance of textual features, the second item type having a lower importance of visual features and a greater importance of textual features (see at least Joshi ¶¶0020-0021, 0027, 0037. Please note: these limitations appear to be directed to anecdotal examples following from arbitrarily selected example products and are therefore implicitly disclosed by the prior art as it would be understood by a person of ordinary skill in the art. They are also examples of obvious design choice.).Claim 24. The computing device of claim 23, wherein the text weight value of the first item type is lower than the text weight value of the second item type, and the image weight value of the first item type is greater than the image weight value of the second item type (see at least Joshi ¶¶0020-0021, 0027, 0037. Please note: see previous comment).
Claim 28. The computing device of claim 21, wherein the selecting at least one of the items from the database having a highest weighted score includes selecting a plurality of items and causing the user device to display at least one of the plurality of items having a different item type than an item type of the anchor (see at least Joshi ¶0037).
Pertaining to method claims 29-31 and 36
Rejection of claims 29-31 and 36 is based on the same rationale noted above. In addition Joshi in view of Zhang discloses with regard toClaim 29. A method, comprising:
● generating similarity scores between the anchor and each item in the database based on a text embedding and image feature of the anchor and a text embedding and image feature of each item, wherein the text embeddings of each item comprise vector descriptions of text associated with each item (Joshi teaches this limitation as detailed above with regard to claim 21, but does not explicitly disclose “wherein the text embeddings of each item comprise vector descriptions of text associated with each item,” but see at least Zhang §3.1 generates text embeddings (fixed-length text feature vectors) from product titles and descriptions for each product in the database).
Claim 30. The method of claim 29, wherein calculating the similarity score between text embeddings of the anchor and text embeddings of the plurality of items in a database includes calculating a cosine similarity score between the text embeddings of the anchor and the text embeddings of the plurality of items in the database (see at least Joshi fig. 1 "extracted metadata," 7, ¶0028 "extracted metadata may be encoded as textual characteristics," ¶0030 "cosine similarity may be used as a method for computing similarity").Claim 31. The method of claim 29, wherein calculating the similarity score between the image feature of the anchor and the image feature of the plurality of items in the database includes calculating a cosine similarity score between the image features of the anchor and the image features of the plurality of items in the database (see at least Joshi abstract “extracting concept information from visual content," ¶0030 "cosine similarity may be used as a method for computing similarity").
Claim 36. The method of claim 29, further comprising filtering the items as selected responsive to selection of the anchor to be a different item type than the item type of the anchor (see at least Joshi ¶0037).
Claims 25-27 and 32-35 are rejected under 35 U.S.C. 103 as being unpatentable over Joshi et al. (Pub. No. US 2018/0032882 A1) in view of Zhang et al. (Nonpatent literature identified as item V on attached form PTO-892) as applied to claims 21 and 29 above, and further in view of Li et al. (Patent No. US 10,715,862 B2).
Joshi in view of Zhang teaches all of the above as noted in the rejection and teaches, a) similarity score between image features, b) image features including color, and c) a histogram, but does not explicitly disclose wherein the image feature includes a red-green-blue (RGB) color histogram on the image. Li teaches a) similarity score between image features, b) image features including color, and c) a
histogram, and also teaches wherein the image feature includes a red-green-blue (RGB) color histogram on the image. Li discloses pertaining toClaim 25. The computing device of claim 21, wherein the image feature of each item in the database includes a red-green-blue (RGB) color histogram (see at least Li c6:10-37 "pixel-by-pixel and colour histogram," c6:38-59 "visual features may include colour histograms," c8:30-47 "visual features may include colour histograms and histograms of oriented gradient").Claim 26. The computing device of claim 25, further comprising determining the RGB color histogram for a foreground of the anchor and the plurality of items in the database (see at least Li c6:10-37 "colour histogram difference between consecutive frames are calculated and if the difference exceeds a predetermined threshold, the frames are separated into two separate shots," c6:38-59 "different views such as frontal").Claim 27. The computing device of claim 25, wherein RGB channels associated with the RGB color histograms include 8 bins per channel to obtain a 512-dimensional feature vector for the anchor and the plurality of items in the database (see at least Joshi figs. 3-4, ¶¶0021-0024, in view of Li c10:9-67).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Joshi in view of Zhang to include wherein the image feature includes a red-green-blue (RGB) color histogram on the image, as taught by Li since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Li in the method of Joshi in view of Zhang. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Pertaining to method claims 32-35
Rejection of claims 32-35 is based on the same rationale noted above. In addition Joshi in view of Zhang and further in view of Li discloses with regard toClaim 32. The method of claim 29, further comprising separating a background and foreground of an image associated with each item in the database (Joshi in view of Zhang does not disclose this limitation but see at least Li c6:10-37 "the frames are separated into two separate shots").Claim 33. The method of claim 32, wherein the separating comprises a mean adaptive threshold (Joshi in view of Zhang does not disclose this limitation but see at least Li c6:10-37 "if the difference exceeds a predetermined threshold, the frames are separated into two separate shots").Claim 34. The method of claim 32, wherein the image feature of each item in the database includes a red-green-blue (RGB) color histogram, the method further comprising determining the RGB color histogram for the foreground of each image following the separating the background and the foreground of each image associated with each item in the database (Joshi in view of Zhang does not disclose this limitation but see at least Li c6:10-59, c8:30-47).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Bhardwaj, non-patent literature identified as item W on the attached form PTO-892: teaches searching by color.
● Cardoso, non-patent literature identified as item U on the attached form PTO-892: teaches at least image and text similarity, determining attributes by product type and weighting attributes.
● Agarwal et al., Pub. No.: US 2018/0247363 A1: teaches an earlier version of the invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571.272.6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ADAM L LEVINE/Primary Examiner, Art Unit 3689 June 25, 2026
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