DETAILED ACTION
Case Status
This office action is in response to amendments of 31 March 2026. Claims 1-17 have been examined and are pending.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 2 March 2022.
Pertinent Prior Art
The prior art made of record and not currently relied upon is considered pertinent to applicant's disclosure:
US 20150254514
Pars. 47-52
Selecting a query feature extracted from the camera video to search videos stored in other video storages
US 20040143602
Pars. 80, 157, 160, 175-179, 185-194
Querying using tracked objects seen in camera video images; Profiles contain data that permits a database to be searched with the parameters that get translated into feature information relating to time difference windows, time frames e.g., from time x to x+10 minutes for events to detect objects such as persons, vehicles, license plates, etc.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-17 are directed to one of the eligible categories of subject matter.
With respect to independent claim 1, 10, 11, the comparing covers performance of the limitations manually and/or in the mind (mental processes abstract idea). The acquiring and loading limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claim 2, 3, 4, 8, 9, 12-17 the positioning, classifying, performing… extraction, performing…analysis, determining, satisfies, expanding cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The acquiring, storing, loading, adding, retrieving are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 5, 6, 7 the comparing, detecting, determining, sorting cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sid Ryan, Pub. No.: US 20230040513 A1, hereinafter Ryan in view of Barykin et al., Pub. No.: US 20150032725 A1, hereinafter Barykin.
As per claim 1, Ryan discloses A target retrieval method, performed by a center server (pars. 68, 69, 73 disclose central server 150), comprising:
acquiring structured feature information that is input when information retrieval is performed on a preset information retrieval database of the center server (pars. 45, 148 wherein a query image includes information about time, location, person, body, posture, clothing, etc. – all of these are human-understandable attributes that are available in a query image; par. 76 discloses that “the [central] server 150 comprises a database 151 and AWS module 221”. Claim interpretation note: “structured feature information” as used in the instant application is directed to human understandable attributes and not feature vectors/embeddings; also, note that the claimed input structured feature information requires, in light of the very next limitation, time/time range info and location/location range info);
acquiring, according to time information and range information contained in the input structured feature information, semi-structured feature information in thermal data within a first preset period and a first preset range from the preset information retrieval database, using the semi-structured feature information as to-be-retrieved thermal data (pars. 141-143 discloses query image timestamp information and expected journey time/predefined time window, as well as expected distances are used to obtain (i.e. acquire), and compared to, embedding vectors (i.e. semi-structured feature information) associated with particular known entities and/or stored images, the known entities/stored images being candidate target data (thermal data) corresponding to the expected journey time/predefined time window (i.e. a first preset period and first preset range). Claim interpretation notes: “thermal data” is described in the published specification as follows: par. 47: “the target feature vector (thermal data)”, par. 52, 59: “semi-structured feature information used as thermal data”, par. 82: “The semi-structured feature information is used as the thermal data”. Thermal data in the application does not refer to infrared heat imagery – it is merely a reference to candidate target data; “semi-structured feature” is described in the published specification as follows: par. 34 “feature vector (that is, semi-structured feature information) is extracted by the deep learning neural network model … through embedding mapping of the deep learning neural network model.”), and loading the semi-structured feature information into a preset thermal data memory […] (pars. 76, 143 discloses maintaining the reduced candidate embedding vectors (i.e., the semi-structured feature information) in already known / predetermined / established database 151 or cloud store (i.e. preset memory) - the candidate embedding vectors being associated with particular known entities and/or stored images, the known entities/stored images being candidate target data (i.e. thermal data)),
Ryan does not expressly disclose, however, Barykin, in the related field of endeavor of data storage and retrieval discloses using a memory cluster as claimed (Barykin fig.’s 1, 4, pars. 56-63 disclose an in-memory data storage module implemented by one or more clusters of computers configured to provide dynamic memory for storage, a leaf node cluster of memory-server nodes, and pars. 45-58, fig.’s 2, 4 disclose loading data into that cluster in that a data ingestion module serializes the incoming data, divides it into distributed chunks, and stores/loads the chunks into the leaf nodes of the in-memory leaf node cluster).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Barykin would have allowed Ryan to use a known technique (in-memory leaf node cluster for storing and querying date) to improve Ryan’s system to have “low latency to ensure effective operation” (Barykin, par. 26).
acquiring a real-time video stream of a camera within a second preset period and a second preset range (Ryan pars. 61, 69-71, 97 disclose real time images from cameras within the ranges of the cameras. Ryan Pars. 140-143, 148-150 disclose re-identification, tracking etc. from real time video at future times (second preset period) from locations/regions (i.e. second preset range) of the cameras that capture the images (such as less than 10 meters travel after 1 second as written in Ryan par. 143));
acquiring semi-structured feature information of a potential target in the real-time video stream (see rejection of above limitations including at least Ryan pars. 86, 95, 126, 148-151); and
comparing the semi-structured feature information of the potential target with the semi-structured feature information in the thermal data memory cluster and determining whether the potential target is a retrieval target according to a comparison result (see rejection of above limitations including at least Ryan pars. 86, 95, 126, 148-151 and see Barykin as cited above for at least memory cluster).
Analogous claims 10-11 are likewise rejected.
As per claim 2, Ryan as modified discloses The target retrieval method according to claim 1, wherein a method for pre-acquiring the information retrieval database comprises: acquiring shot videos of a complete set of cameras in a preset occasion (see at least rejection of acquiring a real-time video stream of a camera limitation of claim 1; claim interpretation notes: “shot videos” is understood as videos captured in the past; “a complete set of cameras” is interpreted as one or more camera, no details recited about what a set is and what it means for it to be “complete”; “preset occasion” is understood to mean any known and identifiable (arbitrary) point in time);
positioning and classifying a totality of preset objects in the shot videos and acquiring an image of each preset object among the totality of preset objects, wherein the preset objects comprise at least one of persons and vehicles (see fig.’s 4-10, 12 for multiple examples of “positioning and classifying”; see also, rejection of 2nd and 3rd limitations of claim 1 where known camera positions are used to obtain images and videos of people, objects and more, all of which are “positioned and classified” as claimed);
performing feature vector extraction on images of the totality of preset objects to acquire semi-structured feature information of the totality of preset objects (see rejection of above limitations including at least pars. 86, 95, 126, 148-151);
performing structured analysis on the semi-structured feature information of the totality of preset objects to acquire structured feature information of the totality of preset objects (see rejection of above limitations including at least pars. 86, 95, 126, 148-151); and
storing, in a preset database, the semi-structured feature information and the structured feature information of the totality of preset objects in correspondence with the preset objects to acquire the information retrieval database (see rejection of above limitations including at least pars. 68-70, 112, 113, 116, 122, 126, 128-130, 140, 151).
As per claim 3, Ryan as modified discloses the target retrieval method according to claim 1, wherein the first preset period comprises at least one of an incident period and a pre-incident period, and the first preset range comprises at least one of an incident range and a range within a preset distance outside the incident range (see rejection of at least 2nd limitation of claim 1 and note that the period and range both correspond to an “incident” which just means “an event or occurrence”); wherein acquiring, according to the time information and the range information contained in the input structured feature information, the semi-structured feature information within the first preset period and the first preset range from the information retrieval database comprises: determining a to-be-searched first preset period and a to-be-searched first preset range according to the incident period and the incident range contained in the input structured feature information, wherein the first preset period comprises at least one of a first preset sub-period and a second preset sub-period; and the first preset range comprises at least one of a first preset sub-range and a second preset sub-range; (see rejection of claim 1 – note that all subsequent time periods (sub-periods) and ranges/distances (sub-ranges) until an end (such as that disclosed in 149) are used as to-be-searched for monitoring, tracking, re-identifying as discussed in the rejection of claim 1); and
acquiring at least one of the following: a totality of semi-structured feature information in a first shot video within the first preset sub-period and the first preset sub-range, and a totality of semi-structured feature information in a second shot video within the second preset sub-period and the second preset sub-range, and wherein a duration difference between the first preset sub-period and the incident period is less than a duration difference between the second preset sub-period and the incident period (see rejection of claim 1 including at least pars. 96, 104, 142, 118, 129); and a distance difference between the first preset sub-range and the incident range is less than a distance difference between the second preset sub-range and the incident range (see rejection of claim 1 including at least pars. 96, 104, 142, 118, 129, 143).
As per claim 4, Ryan as modified discloses The target retrieval method according to claim 1, wherein acquiring the semi-structured feature information of the potential target in the real-time video stream comprises: positioning the potential target in the real-time video stream and acquiring an image of each potential target, wherein the potential target comprises at least one of a person and a vehicle (see rejection of claim 1 including at least pars. 86, 95, 126, 148-151); and performing feature vector extraction on the image of each potential target and acquiring the semi-structured feature information of the potential target (see rejection of claim 1 including at least pars. 86, 95, 126, 148-151).
As per claim 5, Ryan as modified discloses The target retrieval method according to claim 1, wherein comparing the semi-structured feature information of the potential target with the semi-structured feature information in the thermal data and determining whether the potential target is the retrieval target according to the comparison result comprise: comparing a preset parameter of the semi-structured feature information of the potential target with a preset parameter of the semi-structured feature information in the thermal data (see rejection of comparing limitation of claim 1); detecting whether a difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data satisfies a preset requirement (See rejection above including pars. 67, 81, 141, 142, 151, 168); and determining whether the potential target is the retrieval target according to a detection result of the difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data, wherein the preset parameter comprises at least one of a Euclidean distance and a cosine distance (see rejection of previous limitation including at least pars. 67, 81, 142).
As per claim 6, Ryan as modified discloses The target retrieval method according to claim 5, wherein determining whether the potential target is the retrieval target according to the detection result of the difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data comprises: in response to the difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data being less than a first preset threshold, determining that the potential target is the retrieval target (see rejection of claim 5 including at least pars. 67, 81, 142); and in response to the difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data being greater than or equal to the first preset threshold, determining that the potential target is not the retrieval target (see rejection of claim 5 including at least pars. 67, 81, 142).
As per claim 7, Ryan as modified discloses The target retrieval method according to claim 6, wherein the target retrieval method further comprises: in response to the difference value between the preset parameter of the semi-structured feature information of the potential target and the preset parameter of the semi-structured feature information in the thermal data being less than the first preset threshold, sorting difference values between preset parameters of semi-structured feature information of a totality of acquired potential targets and the preset parameter of the semi-structured feature information in the thermal data (see rejection of claim 5 including at least pars. 67, 80-82, 142, 145); and determining similarity sorting of the semi-structured feature information of the potential target and the semi-structured feature information in the thermal data according to a sorting result of the difference values, wherein the smaller a difference value is, the greater a similarity between the semi-structured feature information of the potential target and the semi-structured feature information in the thermal data is (see rejection of claim 5 including at least pars. 67, 80-82, 142, 145).
As per claim 8, Ryan as modified discloses The target retrieval method according to claim 1, wherein a method for loading the thermal data into the preset thermal data memory cluster comprises: loading the thermal data from thermal data of a latest date to a plurality of different cluster nodes successively in a polling manner until each of the plurality of different cluster nodes is loaded with the thermal data up to an upper memory limit (see rejection of claim 5 including at least pars. 67, 80-82, 142, 145. Claim interpretation note: no details whatsoever are provided in the claim (and absolutely no explicit definitions in the specification) concerning “latest date”, how many of the images are of this “latest date”, whether multiple “not latest” dates exist, what “polling manner” is, whether the “upper memory limit” is more than just 1, etc.); and after an image from a shot video within the first preset period and the first preset range is newly added to the information retrieval database for first preset duration, loading semi-structured feature information corresponding to the newly added image into the thermal data memory cluster, wherein the first preset duration is a positive integer multiple λ of a time period T1, and the time period T1 is a time period required from acquiring the newly added image to completing performing structuralization on the newly added image (see rejection of claim 5 including at least Ryan pars. 67, 80-82, 142, 145 and Barykin as cited in the rejection of claim 1).
As per claims 12-17, they include the same language as claim 8 and are therefore likewise rejected.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ryan as modified and further in view of Vlad Cardei, Pub. No.: US 20190130545 A1, hereinafter Cardei.
As per claim 9, Ryan as modified discloses The target retrieval method according to claim 1. Ryan as modified does not expressly disclose however Cardei in the related field of endeavor of image processing discloses wherein the target retrieval method further comprises: in response to the input structured feature information containing a color of the retrieval target and the color of the retrieval target being covered by a light color in an environment, acquiring environmental light color information of a complete set of cameras, and expanding the color of the retrieval target to a primary color and a potential color according to a preset solution (Cardei, pars. 2, 8, 10, 26-30 and see Ryan as cited in the rejection of claim 1); and retrieving semi-structured feature information of images from the information retrieval database, wherein the images corresponds to the expanded color of the retrieval target, using the semi-structured feature information of images as the thermal data and adding the semi-structured feature information of the images to the preset thermal data memory cluster (Cardei, pars. 2, 8, 10, 26-30 and see Ryan and Barykin as cited in the rejection of claim 1).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Cardei would have allowed Ryan as modified to implement a system in which “A luminosity characteristic of the image is adjusted to form an adjusted image. A second set of features of the adjusted image are extracted. A neural network is trained to adjust luminosity characteristics of images using the first set of features and the second set of features of the adjusted image. An exposure adjustment model adjusts luminosity characteristics of images based on correction values determined using the trained neural network” (Cardei, abstract).
Response to Arguments
Applicant's arguments filed 28 October 2025 have been fully considered.
Regarding the 35 USC 101 rejection, pages 15-16 of the remarks include:
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Examiner respectfully disagrees. The claim still broadly recites collecting information, selecting/acquiring information based on time/range information, acquiring video derived feature information, comparing feature information, and determining whether a potential target is a retrieval target. These steps amount to data gathering, data organization/filtering, and comparison/classification, which are abstract mental processes. The argument that the claim provides a concrete technical process of data movement, storage, and computation with a defined machine architecture is unpersuasive because the claim has no details of a particular improved architecture. The claim says nothing about a special arrangement of cluster nodes, a particular polling algorithm, memory limits, load balancing rules, or any particular improvement in how the computer or memory cluster operates. The mere recitation of a “center server” and a “preset thermal data memory cluster” uses generic computer components as tools to perform the abstract idea.
The remarks further present:
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Examiner respectfully disagrees. Claim 1 does not recite reducing I/O latency, reducing bandwidth bottlenecks, avoiding disk or remote database access, performing distributed parallel comparison across nodes, or achieving any measurable improvement in retrieval speed.
The remarks further include:
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Examiner respectfully disagrees. The claim basically stores/loads data in memory and performs a comparison. Storing data and comparing stored data are generic computer functions. The claim does not improve the functioning of any computer or technology. It only improves the abstract idea itself by doing it faster or more efficiently using generic computer components. Also, the additional elements do not amount to significantly more than the abstract idea. The database, center server, camera, memory cluster, etc. are recited at a high level of generality and perform their ordinary functions of receiving data, storing data, loading data, and comparing data.
Regarding the prior art rejection of claim 1, pages 18-19 of the remarks include:
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Examiner respectfully disagrees. These arguments are directed to features not recited in claim 1. The claim does not recite “pre-query conditions,” “pre-filtering,” any ordering of filtering relative to comparison, “targeted querying,” or any mapping of user-input structured information into semi-structured information. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Additionally, Applicants argument that Ryan extracts features automatically from the full dataset and uses time/location only as a “post-filtering condition” is not persuasive. Ryan, par. 143 discloses reducing the candidate set according to image timestamp and expected journey time/travel distance within a predefined time window, and pars. 141-142, 144 disclose comparing the query embedding against the resulting reduced (i.e., pre-filtered) candidates, thereby acquiring, according to time and range info, the to-be-searched (thermal) data within a first preset period and first preset range, as claimed. Note that, as stated above, claim 1 does not actually require “pre-query conditions,” “pre-filtering,” or any ordering of filtering relative to comparison.
Regarding newly added “memory cluster”, Barykin has been added as a new reference.
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 SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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/SYED H HASAN/Primary Examiner, Art Unit 2154
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