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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 07/05/2022. It is noted, however, that applicant has not filed a certified copy of the JP2022-108648 application as required by 37 CFR 1.55.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 88, “the plurality of videos are shot” should be “the plurality of videos is shot”.
Paragraph 187, “Although a plurality of steps (processes) have been” should be “Although a plurality of steps has been”
Appropriate correction is required.
Claim Objections
Claim 7 is objected to because of the following informalities: The claim recites both “detection targets” and “detection target” which makes it unclear if the claim is referring to multiple targets or a singular target. Appropriate correction is required.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3 and 6-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process implemented by generic computer elements. This judicial exception is not integrated into a practical application because the generic computer elements are not significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited, processor and memory are used in a manner that is frequently used in computers with one for executing processes and the other used to store information. The term “engine” is used, but is similarly is broadly used as a form of analysis which renders it generic.
In regards to claim 1, a video analysis apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions to (A person of ordinary skill in the art can analyze a video and a person can store instructions and then execute those instructions): accept selection of a type of engine in order to analyze each of a plurality of videos; detect a detection target included in each of the plurality of videos (A person can analyze a plurality of videos and detect a target, similarly a person could accept or choose a way to analyze the video) and assign a detection target ID, for identifying the detection target, to the detected detection target (A person can then identify what the target is an apply a label to them either mentally or by writing the label down on a piece of paper); analyze appearances of the detection target in the plurality of videos to generate an analyzing result including an appearance feature value of the detection target, the appearance feature value being relating to an appearance attribute of the detection target (A person of ordinary skill in the art can see the appearances of a target and determine a feature value of said target with the value being related to some attribute of the target such as the color, appearance or any other feature); acquire a detection target ID of the detection target, the appearance feature value, and a reliability of the appearance feature value that is generated, by analyzing the plurality of videos by using the selected type of the engine among results of analyzing the plurality of videos by using a plurality of types of the engines (A person can identify a target, know a feature of that target, and determine a form of reliability for said target whether that be that they are simply sure or unsure of the type of the target and may use videos which are viewed via some engines); and integrate the acquired results of analyzing the plurality of videos, wherein the integrating of the acquired results includes: extracting the detection target associated with the reliability being higher than a first threshold (A person of ordinary skill in the art can create a basic threshold for reliability that could be simply, unsure, 50/50, and sure to determine a basis for reliability and extract any target that passes their test): grouping the extracted detection target such that the detection targets associated with a similarity of the appearance feature value being a second threshold or higher form a same group and the detection targets associated with the similarity being less than the second threshold form different groups (A person of ordinary skill in the art can determine a level of similarity between two objects and then create a basic threshold and group objects accordingly); and generating integration information that associates the detection target ID of the detection target with a group to which the detection target belongs (A person of ordinary skill in the art can further create further information of the identified target with its respective group and further associate info and the ID with the group).
In regards to claim 2, wherein the selection of a type of the engine is carried out by selecting a result of analyzing each of the plurality of videos (A person of ordinary skill in the art can select a form of analysis based on the results of analyzing a plurality of videos).
In regards to claim 3, wherein the integrating the acquired results includes integrating results of analyzing the plurality of videos by the same type of the engine (A person can integrate the results of analyzing many videos by the same form of analysis).
In regards to claim 6, wherein: the result of analyzing the plurality of videos further includes imaging identification information for identifying an imaging apparatus shooting the video including the detection target, and the integration information further associates the imaging identification information (A person can further integrate the knowledge of where an imaging apparatus is located along with determining the location via metadata, a GPS, or simply by the angle of the footage and surrounding conditions).
In regards to claim 7, wherein the integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos and computing a number of occurrences of the detection target (A person can count the number of targets in a video and the number of occurrences of a target).
In regards to claim 8, wherein: the result of analyzing the plurality of videos further includes a shooting time during which the video including the detection target is shot (A person of ordinary skill can determine what time the during which the video was shot by looking at the background or at timestamps in the video), and integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos for each time range in which each of the plurality of videos is shot (A person can count the targets and when they appeared within a time range), and computing a number of occurrences of the detection target for each time range (A person can compute the number of times a target appeared during each time frame).
In regards to claims 9 and 10, they are similar to claim 1, and they are similarly rejected.
In regards to claim 11, wherein the integrating of the acquired results further includes acquiring the number of occurrences of the detection target by counting the number of times the detection target is included in the plurality of videos (A person can count the number of times a target has appeared in a video and the number of occurrences of a target).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-3, 6, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Alcock et al. (US 20200082212 A1), hereinafter referred to as Alcock, in view of Hatanaka et al. (US 20180130225 A1), hereinafter referred to as Hatanaka, and in view of Lee et al. (WO 2023128026 A1), hereinafter referred to as Lee.
In regards to claim 1, Alcock discloses a video analysis apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: accept selection of a type of engine in order to analyze each of a plurality of videos (Paragraphs 11, 66, 67, 2 and 10, Paragraph 10 discloses the processor and memory and paragraph 2 discloses video analysis and Paragraphs 66-67 disclose the ability for a user to select an engine and paragraph 11 discloses using it for multiple videos along with the object of interest or detection target); analyze appearances of the detection target in the plurality of videos to generate an analyzing result including an appearance feature value of the detection target, the appearance feature value being relating to an appearance attribute of the detection target (Paragraph 44, This paragraph discloses that the appearance of the object is used to determine its type with an exemplary of the gait analysis reading upon an appearance feature value); the appearance feature value, by analyzing the plurality of videos by using the selected type of the engine among results of analyzing the plurality of videos by using a plurality of types of the engines (Paragraph 44, This paragraph discloses that the appearance of the object is used to determine its type with an exemplary of the gait analysis reading upon an appearance feature value).
However, Alcock does not explicitly disclose detect a detection target included in each of the plurality of videos and assign a detection target ID, for identifying the detection target, to the detected detection target; acquire a detection target ID of the detection target, and a reliability of the appearance feature value that is generated, and integrate the acquired results of analyzing the plurality of videos, wherein the integrating of the acquired results includes: extracting the detection target associated with the reliability being higher than a first threshold: grouping the extracted detection target such that the detection targets associated with a similarity of the appearance feature value being a second threshold or higher form a same group and the detection targets associated with the similarity being less than the second threshold form different groups; and generating integration information that associates the detection target ID of the detection target with a group to which the detection target belongs.
Hatanaka does disclose detect a detection target included in each of the plurality of videos and assign a detection target ID, for identifying the detection target, to the detected detection target (Paragraphs 36-37, Discloses that an ID is stored that identifies the object that needs to be identified); acquire a detection target ID of the detection target (Paragraphs 36-37, Discloses that an ID is stored that identifies the object that needs to be identified), grouping the extracted detection target such that the detection targets associated with a similarity of the appearance feature value being a second threshold or higher form a same group and the detection targets associated with the similarity being less than the second threshold form different groups (Paragraph 52, Discloses that the commodities are grouped via their similarity values being greater than the threshold or less than the threshold); and generating integration information that associates the detection target ID of the detection target with a group to which the detection target belongs (Paragraphs 52 and 36-37, Paragraph 52 discloses that the unknown commodity is recognized as one of the recognized commodities and paragraphs 36-37 disclose that an ID is stored that identifies the object that needs to be identified).
It would be prima facie obvious to combine the teachings of Alcock and Hatanaka as it would have led to a predictable increase in accuracy as the labeling of a specific ID for each target would allow for more accurate tracking of each target. Alcock discloses that the object is identified, but Alcock does not explicitly disclose that a specific ID is assigned to the identified object. The inclusion of Hatanaka’s ID allows for better tracking and more readily available identification. As such, it would be prima facie obvious to combine these two arts.
However, Hatanaka does not explicitly disclose and a reliability of the appearance feature value that is generated, and integrate the acquired results of analyzing the plurality of videos, wherein the integrating of the acquired results includes: extracting the detection target associated with the reliability being higher than a first threshold.
Lee does disclose a reliability of the appearance feature value that is generated (Abstract, disclose that reliability is calculated via detections in target object areas), and integrate the acquired results of analyzing the plurality of videos, wherein the integrating of the acquired results includes: extracting the detection target associated with the reliability being higher than a first threshold (Last Paragraph of Page 2 and first and second paragraphs of page 3, Discloses that reliability can be used as a threshold to separate objects which are then extracted).
It would be prima facie obvious to combine the teachings of the prior arts as the inclusion of reliability would allow for a predictable increase in accuracy. Accounting for the reliability of each of the detections allows for an increase in accuracy as the various identifications can be judged on whether or not the identified object is identified accurately. This prevents inaccurate groupings by preventing less reliable identifications influence the groupings. As such, it would be prima facie obvious.
In regards to claim 2, Alcock discloses wherein the selection of a type of the engine is carried out by selecting a result of analyzing each of the plurality of videos (Paragraphs 66-67, Paragraph 66 discloses the refine results concept which would allow the engine selection process to be based the result of analyzing the image).
In regards to claim 3, Alcock discloses wherein the integrating the acquired results includes integrating results of analyzing the plurality of videos by the same type of the engine (Paragraph 67, The ability for the user to select which engines can be combined as disclosed in paragraph 67 covers this claim).
In regards to claim 6, Alcock discloses wherein: the result of analyzing the plurality of videos further includes imaging identification information for identifying an imaging apparatus shooting the video including the detection target, and the integration information further associates the imaging identification information (Paragraph 56, The metadata and reference coordinates of the video are clearly disclosed and to combine this data with the integration process disclosed in the document).
In regards to claims 9-10, they are similar to claim 1, and they are similarly rejected.
Claims 7-8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Alcock et al. (US 20200082212 A1), hereinafter referred to as Alcock, in view of Hatanaka et al. (US 20180130225 A1), hereinafter referred to as Hatanaka, and in view of Lee et al. (WO 2023128026 A1), hereinafter referred to as Lee as applied to claims 1-3, 6, and 9-10 above, and further in view of Kedarisetti et al. (US 20240273736 A1), hereinafter referred to as Kedarisetti.
In regards to claim 7, Alcock does not disclose wherein the integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos and computing a number of occurrences of the detection target.
However, Kedarisetti does disclose wherein the integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos and computing a number of occurrences of the detection target (Paragraph 212 and 218, Kedarisetti discloses counting the cells and target in a video).
It would have been prima facie obvious to combine the teachings of Kedarisetti and Alcock as it would have led to a predictable increase in tracking ability. Keeping a count of all of the tracked targets would allow for a user to better compensate for crowds as more people would make tracking more difficult as such, knowing the size of the crowd at any given time would be important for tracking specific people inside of the crowd and how easy that would be. Therefore, it would have been prima facie obvious to combine the teachings of Kedarisetti and Alcock.
In regards to claim 8, Alcock does disclose wherein: the result of analyzing the plurality of videos further includes a shooting time during which the video including the detection target is shot (Paragraph 56, The timestamps described would read on the time range of the detection target), for each time range in which each of the plurality of videos is shot (Paragraph 56, The timestamps described would read on the time range of the detection target).
Alcock does not disclose and integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos and computing a number of occurrences of the detection target for each time range.
However, Kedarisetti discloses integrating the acquired results further includes counting a number of the detection targets included in the plurality of videos (Paragraph 212 and 218, Kedarisetti discloses the counting of targets) and computing a number of occurrences of the detection target for each time range (Paragraph 212 and 218, the process of Kedarisetti can be simply used to).
In regards to claim 11, Kedarisetti does disclose wherein the integrating of the acquired results further includes acquiring the number of occurrences of the detection target by counting the number of times the detection target is included in the plurality of videos (Paragraph 212 and 218, Kedarisetti discloses counting the cells and target in a video).
Response to Amendment
The amendments, entered 1/13/2026, have been considered in full. The amendments made overcame the 102 rejection and the objections to the title. However, a new 103 rejection covers all of the amended material in the claims.
Response to Arguments
Applicant's arguments filed 1/13/2026 have been fully considered but they are not persuasive. The arguments made against the 101 rejection are not persuasive. Examiner in the interview according to the summary made, only indicated that their suggestions would move the case in the right direction according to the interview summary from the Examiner. As such, it would not be enough to necessarily overcome the 101 rejection depending on their implementation in an amended claim. Argument further recites that paragraphs 176-178 integrate the application into a practical application. An exemplary embodiment of what the claims could do that is only in the specification is not enough to overcome a 101 rejection. Further, merely utilizing the results of analyzing a plurality of videos is well-within the bounds of a mental process as people of ordinary skill in the art can watch multiple videos and analyze them accordingly. As such, the arguments presented in regards to the 101 rejections are not persuasive.
Applicant’s arguments with respect to claim 1 in regards to 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONOR AIDAN O'MALLEY whose telephone number is (571)272-0226. The examiner can normally be reached Monday - Friday 9:00 am. - 5:00 pm. EST.
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, Andrew Moyer can be reached at 5722729523. 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.
CONOR AIDAN. O'MALLEY
Examiner
Art Unit 4146
/CONOR A O'MALLEY/ Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675