DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The Action is responsive to Applicant’s amendment, filed on March 12, 2026.
3. It is acknowledged that as a result of the amendment, Claims 1-2, 9, and 13-14, and have been amended.
4. Claims 1-20 are pending.
Response to Arguments
5. Applicant’s arguments with respect to claims 1-20 have been considered but are not persuasive at least for the following reason: Applicant argues in substance that “LI is a Chinese patent application publication, with a publication date of 2023-11-10 (November 10, 2023), which is later than the effective filing date of the present application of October 13, 2023. Thus, LI does not qualify as a prior art reference.” However, LI was filed on September 29, 2022, qualifies as prior art. The AIA defines the term "effective filing date" for a claimed invention in a patent or application for patent (other than a reissue application or... claims for priority to, or the benefit of, the filing date of a prior-filed foreign or domestic application in international design applications). The one-year grace period (as defined in MPEP § 2151). This includes international filing dates claimed as foreign priority dates under 35 U.S.C. 365(a). The Claim Rejections - 35 USC § 112 are withdrawn based on the applicant’s explanation coinciding with the previous Examiner’s interpretation and the cited paragraph in the Remarks dated 3/12/2026.
In view of the above, the Examiner contends that all limitations as recited in the claims have been addressed in this Action. For the above reasons, the Examiner believes that the rejections of the last Office action were proper.
Claim Rejections - 35 USC § 103
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claim(s) 1-5 and 9-17, and is/are rejected under 35 U.S.C. 103 as being unpatentable over GUAN; Lijuan (US 20230350940), in view of LI, Gong-fu (CN 117036718 A), filed on September 29, 2022; hereinafter GUAN and LI, respectively.
As per Claim 1, GUAN discloses:
A processing method comprising: obtaining character information, the character information being used to represent a search target; (Par [0007], “and obtaining, based on the at least one retrieval feature image, a target object image set from a second database including a plurality of object images, to be recommended to the target user.” And abstract “The retrieval image can include a plurality of feature images, and a target object image…”) obtaining an image set, the image set including a plurality of images; (Par [0059], “the at least one feature image obtained from the first image database may be a plurality of images corresponding to the first classification…” the plurality of obtained images being an image set) and based on the character information, the image set, and an intelligent engine, obtaining an image search result including: based on the character information and a first model in the intelligent engine, obtaining a first set, the first set including a plurality of first images; (Par [0053], “…each of the plurality of feature images corresponds to one of a plurality of classification features, and the retrieval feature includes a first classification feature corresponding to the retrieval object in the plurality of classification features” and see Figures 3-5)
and based on the first set, the image set, and a second model in the intelligent engine, obtaining a second set, the second set including a plurality of second images, the second images being used as image search results, and the first model being different from the second model. (Paragraphs [0064]-[0065], “obtaining a second similarity between each of the at least one retrieval feature image and each of the plurality of object images” and par [0102], “various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms” and Claim 22, “wherein the obtaining the target object image set from the second database including the plurality of object images comprises: obtaining a second similarity between each retrieval feature image of the at least one retrieval feature image and each object image of the plurality of object images; obtaining one or more first object images from the plurality of object images, wherein a maximum value of at least one second similarity corresponding to each first object image of the one or more first object images is greater than a second threshold; and obtaining the target object image set based on the one or more first object images”.).
GUAN discloses various dedicated computing units that run machine learning model algorithms, however do not specifically states the models are different and the “wherein: the first model is configured to match a text feature with an image feature and the second model is configured to match an image feature with an image feature”
LI, discloses the above claimed features as follows: wherein: the first model is configured to match a text feature with an image feature; (Abstract, “a matching method and device of text and image” image characteristic model and text characteristic model, see Abstract and page 2, par 6)
the second model is configured to match an image feature with an image feature; (Page 6, second paragraph, “…and sending an image retrieval request for the text and the image to the server” and image characteristic model and text characteristic model, see Abstract and page 2, paragraph 6)
(image characteristic model and text characteristic model, see Abstract and page 2, par 6, “through the self-attention layer of the characteristic model, based on the model parameter of the characteristic model, constructing an attention parameter, and based on the attention parameter, respectively performing attention processing on the image characteristic and the text characteristic, obtaining the image attention characteristic of the image, and a text attention characteristic of the text”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of LI specifically the recitation of two different models related to a specific function into the method of GUAN to take advantage on applying a specific special space with its specific model, according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement providing different models to determine similarity between text hash feature or image has features.
As per Claim 2, the rejection of Claim 1 is incorporated and LI further discloses:
wherein a degree of matching between the second set and the character information is higher than a degree of matching between the first set and the character information. (Page 3, par 8, “obtaining the text characteristic to be matched and the corresponding image characteristic through the characteristic extracting layer of the characteristic model…through the matching layer of the feature model, based on the text hash feature and the image hash feature, obtaining the matching degree of the text and the image to be matched, The matching mode of the text and the image based on the Hash feature can greatly reduce the calculation amount and the consumption of the calculation resource, so the matching precision between the text and the image can be improved, and the matching efficiency between the text and the image can be improved.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of LI specifically the recitation of two different models related to a specific function into the method of GUAN to take advantage on applying a specific special space with its specific model, according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement providing different models to determine similarity between text hash feature or image has features, with a respective matching degree.
As per Claim 3, the rejection of Claim 1 is incorporated and LI further discloses: wherein, based on the character information and the first model in the intelligent engine, obtaining the first set includes: extracting the text features of the character information based on the first model; (Page 3, paragraph 1, “through the characteristic extracting layer of the characteristic model to obtain the corresponding image characteristic and text characteristic”) and matching the text feature with an image feature of each image in the image set based on the first model to obtain the first set satisfying a first condition. (Page 3, paragraph 1, “through the characteristic extracting layer of the characteristic model to obtain the corresponding image characteristic and text characteristic” and Page 10, paragraph 10, “in the step 102, through the self-attention layer of the characteristic model, based on the model parameter of the characteristic model, constructing the attention parameter, and based on the attention parameter, respectively performing attention processing on the image characteristic and the text characteristic, obtaining the text attention characteristic of the text, and image attention characteristics of the image.” The “parameter” being the “condition” as claimed.).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of LI specifically the recitation of two different models related to a specific function into the method of GUAN to take advantage on applying a specific special space with its specific model, according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement providing different models to determine similarity between text hash feature or image has features, with a respective matching degree.
As per Claim 4, the rejection of Claim 2 is incorporated and LI further discloses: wherein, based on the character information and the first model in the intelligent engine, obtaining the first set includes: extracting the text feature of the character information based on the first model; (Page 3, paragraph 1, “through the characteristic extracting layer of the characteristic model to obtain the corresponding image characteristic and text characteristic” Abstract and page 2, par 6) obtaining an image feature matching the text feature based on the first model; (Page 27, Claim 4, page, “obtaining the image attention feature of the image; performing feature splicing on each text attention sub-feature, and performing feature dimension reduction on the spliced text feature obtained by splicing, obtaining text attention feature of the text. image characteristic model and text characteristic model, see Abstract and page 2, par 6) and generating the first set satisfying the first condition based on the image feature matching the text feature. (Page 10, paragraph 10, “in the step 102, through the self-attention layer of the characteristic model, based on the model parameter of the characteristic model, constructing the attention parameter, and based on the attention parameter, respectively performing attention processing on the image characteristic and the text characteristic, obtaining the text attention characteristic of the text, and image attention characteristics of the image.” Abstract and page 2, par 6. The “parameter” being the “condition” as claimed.).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of LI specifically the recitation of two different models related to a specific function into the method of GUAN to take advantage on applying a specific special space with its specific model, according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement providing different models to determine similarity between text hash feature or image has features, with a respective matching degree.
As per Claim 5, the rejection of Claim 2 is incorporated and GUAN further discloses:, wherein, based on the first set, the image set, and the second model in the intelligent engine, obtaining the second set includes: processing the first set to obtain N subsets, each subset corresponding to a category; (Par [0065], “step S510: obtaining a second similarity between each of the at least one retrieval feature image and each of the plurality of object images;” the second similarity creating subset)
matching image features of M subsets with an image feature of each image in the image set to obtain the second set satisfying a second condition; (Par [0043], “and then the target object image set is obtained by matching the feature image with an object image database, to be recommended to the target user. Due to high definition and a good shooting angle of the feature images in the first database, a feature of the retrieval object is better reflected, such that the object image obtained based on the feature image” matching features, would get another set)
However, GUAN do not specifically disclose the groups being subsets, a category, and satisfying second condition, and the following: wherein: M and N are positive integers greater than or equal to 1; M is less than or equal to N; and the M subsets are obtained sequentially after processing some first images in the first set, or the M subsets are obtained by processing all first images in the first set. (Page 13, paragraphs 2-4, “wherein each attention force sub-parameter is a part of the model parameter of the characteristic model, the attention subparameter is the subset of the model parameter of the characteristic model, the dimension of the attention sub-parameter can be determined according to the dimension of the middle text (or image)” and FIG. 13, FIG. 13 is a schematic diagram of the processing flow of each attention sub-layer provided by the embodiment of the present application, and the operations performed by each attention sub-layer are described in conjunction with the steps shown in FIG. 13. Step 501, the server respectively performs attention processing on the image feature and the text feature based on the attention sub-parameter of the attention sub-layer to obtain the image attention sub-feature of the image and the text attention sub-feature of the text. characteristic and the dimension of the attention sub-characteristic output by the attention sub-layer, the selection mode of the attention sub-parameter can be randomly selected.” And page 15, paragraphs 6-7, “and sequentially executes the steps 101 to 104 shown in FIG. 3, after obtaining the text hash feature of the search text and the image hash feature of each candidate image, The server determines the matching degree of the search text and each candidate image, because the matching degree is measured by the similarity, that is, the server determines the similarity (cosine similarity or hamming distance) between the text hash characteristic of the search text and the image hash characteristic of each candidate image. In step 602, the server orders each candidate image according to the order of the matching degree from large to small, so as to obtain an ordering result.” Complying with the M having all the subsets processing images from the first set).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of LI specifically the recitation of two different models related to a specific function into the method of GUAN to take advantage on applying a specific special space with its specific model, according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement providing different models to determine similarity between text hash feature or image has features, with a respective matching degree.
As per Claims 9-16, being the non-transitory computer readable storage medium and device claims corresponding to the method claims 1-5 respectively and rejected under the same reason set forth in connection of the rejections of Claims 1-5 and further GUAN discloses: (Par [0006]).
Allowable Subject Matter
8. Claims 6-8 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Hunter; Edward (US-20210073291-A1) relates to Some embodiments may then update the symbolic AI model when it is in the second state based on the second simulated input in order to advance the second model to a terminal state, where a terminal state is one that satisfies a terminal state criterion. Once in a terminal state, the symbolic AI system may update the weighting values associated with the symbolic AI model before performing another iteration of the simulation. And also DIMENSIONAL REDUCTION OF CATEGORIZED DIRECTED GRAPHS (US 2021/0073287), relates to B-21 to B-22, wherein obtaining the plurality of scenarios comprises: determining a first simulated input for a first model of the plurality of symbolic AI models based on a multi-iteration score associated with the first model, wherein the first model is in a first state before updating the first model based on the first simulated input; update the first model based on the first simulated input to advance the first model to a second state, wherein the second state is different from the first state; determine a second input, wherein the second input may be selected based on scores associated with each of a set of possible states associated with the first state; update the first model when it is in the second state based on the second input to advance the second model to a third state.
HE; Tao (US-20200090539-A1), relates to identify the characters in the region of the individual questions based on the separate images of the regions of the individual questions on the test paper, the type of the question corresponding to the region of the question, the component corresponding to the region of the question, and a second model, so as to determine the information recorded on the test paper, wherein the second model is a neural network based model.
10. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELICA RUIZ whose telephone number is (571)270-3158. The examiner can normally be reached M-F 10:00 am to 6:00 pm.
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, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANGELICA RUIZ/Primary Examiner, Art Unit 2154 June 11, 2026
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