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 .
Election/Restrictions
Applicant’s election with traverse of Group I, claims 1-10, in the reply filed on 4/26/2026 is acknowledged. Applicant’s arguments are persuasive and the requirement for restriction has been withdrawn.
Claims Status
Claims 1-20 are pending and rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-20 are directed to a method, which is a process. Therefore, claims 1-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Representative claim 1 sets forth the following limitations which recite the abstract idea of presenting product recommendations:
obtaining a first feature vector set corresponding to a first object and at least one second feature vector set corresponding to at least one second object, wherein the feature vector set records image feature vectors corresponding to object images that include the objects, and text feature vectors corresponding to key characteristic description text that describes key characteristics of the objects, wherein the image feature vectors and the text feature vectors correspond to the same feature space
determining an object similarity between the first object and the second object based on multiple vector similarities between the feature vectors in the first feature vector set and the feature vectors in the second feature vector set, wherein the multiple vector similarities include vector similarity between the image feature vectors and the text feature vectors;
screening the at least one second object based on the object similarity to obtain an associated object of the first object.
The recited limitations above set forth steps to present product recommendations. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner notes that representative claim 1 fails to recite additional elements such as a processor, memory, etc.
However, even if additional features such as these were recited, the claims would fail to integrate the recited judicial exception into a practical application of the exception. The claims would merely include instructions to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements would do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also 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 a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply 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 (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 would not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, even including an additional element of a processor or computer to perform the steps would amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even considered as an ordered combination, any additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-10 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for presenting product recommendations. Thus, each of claims 2-10 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-10 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
Claims 11-20 recite similar subject matter as claims 1-20. As such, claims 11-20 are rejected for at least similar rationale as discussed above.
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 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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kislyuk et al. (U.S. Pre-Grant Publication No. 2019/0095467) (“Kislyuk”), in view of Miklos et al. (U.S. Pre-Grant Publication No. 2024/0028638) (“Miklos”).
Regarding claim 1, Kislyuk teaches a method for screening associated objects, comprising:
obtaining a first feature vector set corresponding to a first object and at least one second feature vector set corresponding to at least one second object, wherein the feature vector set records image feature vectors corresponding to object images that include the objects, and text feature vectors corresponding to key characteristic description text that describes key characteristics of the objects (para [0022], portion of the image that includes the object of interest may be segmented from the remainder of the image, the object of interest determined and/or an object feature vector representative of the object of interest generated. Based on the determined object of interest and/or the object feature vector, stored feature vectors of segments of other stored images may be compared with the object feature vector of the object of interest to determine other images that include objects that are visually similar to the object of interest; para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object);
determining an object similarity between the first object and the second object based on multiple vector similarities between the feature vectors in the first feature vector set and the feature vectors in the second feature vector set, wherein the multiple vector similarities include vector similarity between the image feature vectors and the text feature vectors (Fig. 4; para [0056], generated object feature vector and/or label may then be compared with stored feature vectors corresponding to objects represented in segments of stored images to produce a similarity score between the object feature vector and each stored feature vector);
screening the at least one second object based on the object similarity to obtain an associated object of the first object (Fig. 4; para [0060], multiple results of stored images are returned, for example to the user device, based on the ranked results list).
Although Kislyuk teaches obtaining feature vector sets, Kislyuk does not explicitly teach wherein the image feature vectors and the text feature vectors correspond to the same feature space.
In a similar field of endeavor, Miklos teaches wherein the image feature vectors and the text feature vectors correspond to the same feature space (para [0035], machine-learned model 120 may process textual content and one or more query images to determine a multimodal search query; para [0072], multimedia interface element 602C may also include textual content, image(s), link(s), audio data, and/or any other sort of content retrieved responsive to the multimodal search query).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, 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 have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the noted limitations as taught by Miklos in the method of Kislyuk, 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, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, an improvement in visual search applications to accept additional modes of input and interpret a variety of user intents (See Miklos: para [0002]).
Regarding claim 2, Kislyuk and Miklos teach the above method of claim 1. Kislyuk also teaches wherein the obtaining of the first feature vector set corresponding to the first object, and the obtaining of the second feature vector set corresponding to at least one second object, comprises:
in a predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the first object to obtain an image feature vector and a text feature vector corresponding to the first object, and constructing the first feature vector set (para [0031], object of interest may be extracted or segmented from the other portions of the image 101 and the object feature vector may be generated such that the object feature vector is representative of only the object of interest; para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object);
in the predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the second object to obtain an image feature vector and a text feature vector corresponding to the second object, and constructing the second feature vector set (para [0031], object of interest may be extracted or segmented from the other portions of the image 101 and the object feature vector may be generated such that the object feature vector is representative of only the object of interest; para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object).
Regarding claim 3, Kislyuk and Miklos teach the above method of claim 2. Kislyuk and Miklos also teach
wherein, in a predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the first object to obtain an image feature vector and a text feature vector corresponding to the first object comprises:
when the key characteristic description text corresponding to the first object does not belong to specified language text, converting the key characteristic description text corresponding to the first object into the specified language text to obtain first text (see Miklos: para [0045], multimedia interface element 602C may also include textual content, image(s), link(s), audio data, and/or any other sort of content retrieved responsive to the multimodal search query);
performing feature extraction on the first text and the object image corresponding to the first object to obtain the image feature vector and the text feature vector corresponding to the first object (see Kislyuk: para [0031], object of interest may be extracted or segmented from the other portions of the image 101 and the object feature vector may be generated such that the object feature vector is representative of only the object of interest; para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object);
wherein in the predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the second object to obtain an image feature vector and a text feature vector corresponding to the second object comprises:
when the key characteristic description text corresponding to the second object does not belong to specified language text, converting the key characteristic description text corresponding to the second object into the specified language text to obtain second text (see Miklos: para [0045], multimedia interface element 602C may also include textual content, image(s), link(s), audio data, and/or any other sort of content retrieved responsive to the multimodal search query);
performing feature extraction on the second text and the object image corresponding to the second object to obtain the image feature vector and the text feature vector corresponding to the second object (see Kislyuk: para [0031], object of interest may be extracted or segmented from the other portions of the image 101 and the object feature vector may be generated such that the object feature vector is representative of only the object of interest; para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object).
Regarding claim 4, Kislyuk and Miklos teach the above method of claim 2. Miklos also teaches wherein, in a predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the first object to obtain an image feature vector and a text feature vector corresponding to the first object comprises:
inputting the object image and the key characteristic description text corresponding to the first object into a trained image-text multimodal model, and obtaining the image feature vector and the text feature vector output by the image-text multimodal model as the image feature vector and the text feature vector corresponding to the first object (para [0035], machine-learned model 120 may process textual content and one or more query images to determine a multimodal search query; para [0072], multimedia interface element 602C may also include textual content, image(s), link(s), audio data, and/or any other sort of content retrieved responsive to the multimodal search query).
Regarding claim 5, Kislyuk and Miklos teach the above method of claim 4. Miklos also teaches wherein the object comprises a product, and prior to, in a predetermined feature space, performing feature extraction on an object image and key characteristic description text corresponding to the first object to obtain an image feature vector and a text feature vector corresponding to the first object, the method further comprises:
obtaining a product title configured by a merchant for the first object (para [0065], result image 402A includes a title and URL of a web page that hosts the result image 402A. For another example, a result image may include a short descriptor determined based on a machine learned analysis of the result image);
determining the product title corresponding to the first object as the key characteristic description text corresponding to the first object (para [0065], result image 402A includes a title and URL of a web page that hosts the result image 402A. For another example, a result image may include a short descriptor determined based on a machine learned analysis of the result image).
Regarding claim 6, Kislyuk and Miklos teach the above method of claim 1. Kislyuk also teaches wherein determining the object similarity between the first object and the second object based on multiple vector similarities between the feature vectors in the first feature vector set and the feature vectors in the second feature vector set comprises:
obtaining similarity weights configured for the multiple vector similarities (para [0058], Comparison of the object feature vector with stored feature vectors produces similarity scores indicating the similarity between the object feature vector and the stored feature vector with which it is compared);
calculating the object similarity using the multiple vector similarities and the similarity weights corresponding to each vector similarity (para [0058], the similarity score of an image having multiple stored feature vectors that are compared with the object feature vector may be the median similarity score, the lowest similarity score, or any other variation of the similarity scores for feature vectors associated with that stored image).
Regarding claim 7, Kislyuk and Miklos teach the above method of claim 6. Kislyuk also teaches wherein the multiple vector similarities further comprise:
the vector similarity between the image feature vector corresponding to the first object and the image feature vector corresponding to the second object (para [0058], Comparison of the object feature vector with stored feature vectors produces similarity scores indicating the similarity between the object feature vector and the stored feature vector with which it is compared); and
the vector similarity between the text feature vector corresponding to the first object and the text feature vector corresponding to the second object (para [0058], Comparison of the object feature vector with stored feature vectors produces similarity scores indicating the similarity between the object feature vector and the stored feature vector with which it is compared).
Regarding claim 8, Kislyuk and Miklos teach the above method of claim 1. Kislyuk also teaches wherein determining the object similarity between the first object and the second object based on multiple vector similarities between the feature vectors in the first feature vector set and the feature vectors in the second feature vector set comprises:
obtaining a text similarity between attribute description text corresponding to the first object and attribute description text corresponding to the second object (para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object);
calculating the object similarity based on the multiple vector similarities and the text similarity (para [0058], Comparison of the object feature vector with stored feature vectors produces similarity scores indicating the similarity between the object feature vector and the stored feature vector with which it is compared).
Regarding claim 9, Kislyuk and Miklos teach the above method of claim 8. Kislyuk also teaches wherein, prior to obtaining the text similarity between the attribute description text corresponding to the first object and the attribute description text corresponding to the second object, the method further comprises:
obtaining attribute information corresponding to the first object and attribute information corresponding to the second object (para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object);
describing the attribute information corresponding to the first object and the attribute information corresponding to the second object according to a specified document structure to obtain the attribute description text corresponding to the first object and the attribute description text corresponding to the second object (para [0047], Attributes may include, but are not limited to size, shape, color, texture, pattern, etc., of the object. In other implementations, a set of object attributes (e.g., color, shape, texture) may be determined for each object in the image and the set may be concatenated to form a single feature vector representative of the object).
Regarding claim 10, Kislyuk and Miklos teach the above method of claim 1. Kislyuk also teaches wherein screening the at least one second object based on the object similarity to obtain the associated object of the first object from comprises:
obtaining a pre-configured similarity threshold (para [0059], Based on the similarity scores determined for each image, a ranked list of stored images is generated);
screening the at least one second object based on the similarity threshold and the object similarity to obtain the associated object (Fig. 4; para [0060], multiple results of stored images are returned, for example to the user device, based on the ranked results list).
Regarding claims 11-20, claims 11-20 recite substantially similar subject matter as claims 1-10 and are therefore rejected on similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday through Friday, 9am – 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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.
/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
Prosecution Insights