DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 9 December 2025.
Claim(s) 1-20 are currently pending and have been examined.
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
This application claims priority of US Provisional Application No. 63/730262 filed on 10 December 2024. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 16 December 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims not listed below are rejected for dependency.
Claim 1 recites transform the performance data and the clip to an embedding vector for an embedding space stored in a data repository; and update the embedding space to provide, in response to a search query related to performance of the medical procedure, access to at least a portion of the video stream of the medical procedure. It is unclear what it means to “update the embedding space” which makes the claim indefinite.
The ordinary meaning of “update an embedding space” refers to updating the space, i.e., changing the underlying relationship between inputs and embeddings, i.e., changing the function which maps inputs into the embedding space. However, this meaning appears contrary to how the specification uses the term.
The disclosure in [0011] states that “The one or more processors can be configured to update the embedding space with a plurality of embedding vectors constructed for a plurality of clips of a plurality of video streams of a plurality of medical procedures.” The description of the space being updated “with” embedding vectors makes it sound as if the vectors are being added, rather than the space itself being changed. In [0079] the disclosure states “when a new kinematic data is received updating the movement patterns of surgical instruments, ESF 150 can integrate these embedding vectors 176 into the existing embedding space 174. For instance, ESF 150 can facilitate continuous learning by adapting the embedding space 174 in real-time as new data streams 162 or video streams 170 are processed. For example, if a new surgical procedure is introduced and recorded, ESF 150 can incorporate embedding vectors 176 from this data into the embedding space 174, updating the embedding space 174” which strongly suggests that the vectors are simply added to a set of vectors rather than a changing of the space itself.
However, the disclosure in [0079] also mentions that an embedding space function “can adjust the relationships within the embedding space” and can “update the embedding space 174 to reflect new relationships between the embedding vectors 176 corresponding to both the search query 144 and relevant video segments.”
Because of the disclosure’s inconsistent use of the term, both internally and with the ordinary meaning, one of ordinary skill in the art would not be able to determine the meaning of the term, rendering the claim indefinite. Claims 14 and 19 are similarly rejected.
For the purposes of examination, because 1) the claim structure’s generation of an embedding vector prior to the “update” of the embedding space marginally suggests that the update is merely an incorporation of a vector and 2) the lack of written description regarding the alternative possible interpretation, the term will be interpreted as incorporating a determined vector into known set of relationships rather than a change to the relationships and the space itself.
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 an abstract idea without significantly more.
Claim 14, which is representative of claims 1 and 19, recites a method, comprising: identifying,
determining,
transforming,
updating, .
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of receiving data regarding a procedure, evaluating the data, indexing the data, and updating an index to be used for queries. This concept describes a process that an indexer should follow to procedure data similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the managing personal behavior or relationships or interactions between people sub-grouping of methods of organizing human activity. As such, these limitation set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 recites the additional element of a system comprising: one or more processors, coupled with memory. Claim 19 recites the additional element of one or more processors coupled with memory. Claim 20 recites the additional element of a non-transitory computer-readable medium. These additional elements are all recited at a high level of generality and may be interpreted as generic computing devices used to implement the abstract idea Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application. There are no further additional elements. Therefore the additional element of the claim does not integrate the abstract idea into a practical application, and the claim is determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more. There are no further additional elements. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Claims 2-13, 15-18, and 20 recite further limitations which narrow the previously identified method of organizing human activity, but each claim continues to set forth a method of organizing human activity and recite a new abstract idea.
Claim 2 and 15 recite the additional element of receiving data from a system. Receiving information via an electronic device does not represent any improvement to technology, does not require any particular machine, does not effect a transformation, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element, individually and in combination with the prior additional element, only generally links the new abstract idea to a technological environment of networked computing devices. As such, this additional element does not integrate the new abstract idea into a practical application. At Step 2B, per MPEP 2106.05(d)(II), receiving and transmitting data over a network has been recognized by the courts as a conventional computer function. As such, this additional element does not amount to significantly more than the new abstract idea. When considered in combination, the additional elements only generally links the new abstract idea to a technological environment of a networked computing device. As such the combination does not amount to significantly more than the new abstract idea. Because the dependent claims are directed to an abstract idea without reciting significantly more, dependent claims 2 and 15 are not patent eligible.
Claims 3, 16, and 20 recite the additional element of a generative artificial intelligence model and claims 5 and 17 recite the additional element of models trained with machine learning and generative artificial intelligence. These additional elements amount to instructions to implement the new abstract idea with a generic computing device. As previously noted, such additional elements do not integrate an abstract idea into a practical application. As such, these additional element does not integrate the new abstract idea into a practical application. When considered in combination with the previously identified computing device additional element, the additional elements continue to amount to instructions to implement the new abstract idea with a generic computing devices. As such, the claim is determined to be directed to an abstract idea. At Step 2B, these additional element, individually and in combination with the prior identified computing device additional element, still amount to instructions to implement the new abstract idea with a computing device and as such does not amount to significantly more. Because the dependent claims are directed to an abstract idea without reciting significantly more, dependent claims 3, 5, 16, 16, and 20 are not patent eligible.
Claim 5 and 17 recite the additional element of models trained with machine learning and generative artificial intelligence. This additional element amounts to instructions to implement the abstract idea with a generic computing device. As such, this additional element, whether considered individually or in combination with the prior additional element of a computing device, does not either integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Because the dependent claim is directed to an abstract idea without reciting significantly more, dependent claim 3 is not patent eligible.
Claims 6, 10, and 18 recite the additional element of a graphical user interface which receives information and provides information. User interfaces which display and receive information do not represent any improvement to technology, do not require any particular machine, do not effect a transformation, and do not meaningfully limit the implementation of the abstract idea. Instead, this additional element, individually and in combination with the prior additional elements, only generally links the new abstract idea to a technological environment of a computing device. As such, this additional element does not integrate the new abstract idea into a practical application. When considered as a combination, the additional elements only generally link the new abstract idea to a technological environment of a computing device. Thus the combination of additional elements does not integrate the new abstract idea into a practical application. As such, the claim is determined to be directed to an abstract idea. At Step 2B, Geisler et al. (US 2002/0054171 A1) demonstrates (“User interface 102 performs its conventional function of interfacing with the user, e.g. the designer, displaying outputs for the designer and accepting inputs from the designer.” [0024]) that such interfaces were conventional computer elements long before the priority date of the claimed invention. As such, this additional element individually and in combination with the prior identified computing device additional element does not amount to significantly more than the new abstract idea. Because the dependent claims are directed to an abstract idea without reciting significantly more, dependent claims 6, 10, and 18 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-6 and 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2023/0177082 A1) in view of Ayvali et al. (US 2023/0080060 A1) and Kozlov et al. (US 2026/0094442 A1).
Regarding claim 1, 14, and 19: Zhao discloses a system, comprising: one or more processors, coupled with memory (See at least [0006]), to:
identify, for a medical procedure, a video stream and a plurality of data streams related to the medical procedure (receiving the medical operation video that shows a medical operation performed on a patient in the medical operation video; receiving a description of the medical operation. See at least [0004] and [0035]).
determine, based on the plurality of data streams, performance data for a clip of the video stream (deploy object recognition and event recognition models to analyze the surgery video, to generate rich detection results. See at least [0020]).
transform the performance data to an embedding vector for an embedding space stored in a data repository; and update the embedding space to provide, in response to a search query related to performance of the medical procedure, access to at least a portion of the video stream of the medical procedure (To search in the database of medical operation videos, a user-provided text query may be matched against the rich description for each medical operation video in the database (e.g., step 412), using natural language understanding (NLU) methods, such as sentence embedding model, e.g., USE model, BERT model. A machine learning model can be built and/or trained for or by system 100, 200 to rank all the surgery videos (e.g., step 406), based on an input feature representation of the text search query and on input feature representations of each surgery video, into search results for the user to review. The feature representations of the surgery videos (e.g., for step 412) may be based on the results of the various detection tasks performed on the medical operation videos of the user's archive (e.g., from step 410) and based on the user-provided descriptions of the medical operation videos (e.g., from step 408). See at least [0040]. Also: USE model stands for “universal sentence encoder model,” which is a neural network model that embeds natural language sentences into a mathematical high dimensional space. See at least [0038].
Zhao does not expressly disclose that the medical procedure is performed via a robotic medical system. However, Ayvali et al. teaches for a medical procedure performed via a robotic medical system, a video stream (The present disclosure relates to techniques and systems for collecting and analyzing data from robotic assisted medical procedures, such as those performed by a robotic system for stone management (e.g., retrieving urinary stones, suction of stone fragments, etc.) or performing other medical procedures. See at least [0027]. Also: A medical system comprising the robotic system may also allow annotating of video footage of the procedure with metadata identifying the different phases. This allows the video footage to be more easily reviewed by users as well as allowing for more sophisticated analysis of the video footage using artificial intelligence (AI). See at least [0028]. Also: See [0079]).
Zhao provides a system which allows for searching of videos of medical procedures, which differs from the claimed invention by the substitution of Zhao’s generic medical procedure for a medical procedure performed by a robot. However, Ayvali demonstrates that the prior art already knew of processing videos of medical procedure performed by robots to make reviewing them more convenient. One of ordinary skill in the art could have trivially substituted Ayvali’s videos of robot performed medical procedures into the system of Zhao. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would allow the searching of videos of medical procedure performed by robots. As such, the identified substitution would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali.
Zhao does not expressly disclose transforming the clip to an embedding vector. However, Kozlov teaches transform the performance data and the clip to an embedding vector (A vector representation can then be generated 410, using the LLM or a separate encoder, based in part upon the overall summary for the video file and the associated timestamps, as well as image (or other) features extracted from the video file. For example, the vector representation might include image features for objects represented in the video file or audio for sounds in the video file, which can allow for searching or analysis based on those aspects as well. See at least [0051]).
Zhao and Ayvali suggest a system which allows for searching of videos of robot procedures based on embeddings of the descriptions of those videos, upon which the claimed invention’s use of embeddings of both descriptions and the video itself can be seen as an improvement. However, Kozlov demonstrates that the prior art already knew of generating embeddings based on descriptions and video. One of ordinary skill in the art could have easily applied the techniques of Kozlov to the system of Zhao and Ayvali. Further, one of ordinary skill in the art would have recognized that such an application of Kozlov would have resulted in an improved system which would have more information to match queries against by incorporating video embedding data to the matching process. As such, the application of Kozlov, and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali and Kozlov.
Regarding Claim 2 and 15: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Additionally, Ayvali teaches receive, from the robotic medical system, the plurality of data streams comprising at least one of a kinematics data stream, an event stream, or a non-robotic data stream (system 140 receives various input data from the medical system 100. Such input can include video data 305 captured by the imaging sensor 180, robotic sensor data 310 from one or more sensors of the robotic system 110. See at least [0072]. Also: Video data 305 can include video captured from scopes deployed within a patient, video captured from cameras in the operating room, and/or video captured by cameras of the robotic system 110. Robotic sensor data 310 can include kinematic data from the robotic system 110 (e.g., using vibration, accelerometer, positioning, and/or gyroscopic sensors), device status, temperature, pressure, vibration, haptic/tactile features, sound, optical levels or characteristics, load or weight, flow rate (e.g., of target gases and/or liquid), amplitude, phase, and/or orientation of magnetic and electronic fields, constituent concentrations relating to substances in gaseous, liquid, or solid form, and/or the like. See at least [0073]).
Zhao, Ayvali, and Kozlov suggest a system which processes received data regarding video of a robotically performed procedure, upon which the claimed invention’s reception of data from a robotic medical system can be seen as an improvement. However, Ayvali demonstrates that the prior art already knew of receiving data from a robotic medical system. One of ordinary skill in the art could have trivially applied the techniques of Ayvali to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Ayvali would have resulted in an improved system which would have access to more information regarding the robotically performed procedure and thus able to better search such video. As such, the application of Ayvali, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, and Kozlov.
Regarding Claim 3 and 20: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses generate, using a generative artificial intelligence model, the performance data based on the plurality of data streams (deploy object recognition and event recognition models to analyze the surgery video, to generate rich detection results. See at least [0020]).
Regarding Claim 4 and 16: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses wherein the performance data includes a text-based description of the clip generated from the plurality of data streams (deploy object recognition and event recognition models to analyze the surgery video, to generate rich detection results. See at least [0020]).
Regarding Claim 5 and 17: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Additionally, Ayvali teaches generate, using one or more models trained with machine learning, a plurality of performance metrics based on the plurality of data streams; and generate, using generative artificial intelligence, the performance data based on the plurality of performance metrics (Recognition of surgical workflow can be done at different granularity levels, depending on the procedure. It can be done on phases and steps (higher level), or gestures and activities (lower level). Surgical phase recognition can be performed on time series, kinematic data, and video data using machine learning approaches such as HMMs, Gaussian Mixture Models (GMMs), and Support Vector Machines (SVMs), as well as deep learning-based approaches for phase recognition from video data using Convolutional Neural Networks (CNNs). For surgical gesture and activity recognition, similar methods (SVMs, Markov models) can be used on mainly video data or a combination of video and kinematic data, as well as more recent deep-learning based methods such as CNNs that can be used for recognition of tool presence, tasks, and activities in video data. Phase segmentation can use multiple data sources to segment the case data to different subtasks as shown in FIG. 3 or use a single data source, such as video, to classify the current phase as shown in FIG. 4A. In FIG. 4B, additional data (e.g. sensor data or UI data) can then be incorporated to further refine the output produced by the control system 140. See at least [0071]).
Zhao, Ayvali, and Kozlov suggest a system which determines metadata for a video, upon which the claimed invention’s stepped determination of metadata for a video can be seen as an improvement. However, Ayvali demonstrates that the prior art already knew of determining first information regarding a video using machine learning and then using that information and artificial intelligence to determine subsequent information. One of ordinary skill in the art could have trivially applied the techniques of Ayvali to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Ayvali would have resulted in an improved system which would generate superior video description information. As such, the application of Ayvali, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, and Kozlov.
Regarding Claim 6: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses provide a graphical user interface for a search engine; receive, via the graphical user interface, the search query; select, based on execution of the search query on the embedding space, a search result corresponding to the medical procedure; and provide the search result for display via the graphical user interface (Users should be able to articulate their query describing the information need, for example, a text query detailing patient description, surgical tools used, surgery phase, or video snippet query seeking similar surgical situations. See at least [0015]. Also: 4. Video retrieval: The system will or may support retrieval of surgery video or video segments using 2 types of queries: a. Text query: User can provide description of search need (e.g., step 402) including surgery type, surgery phase, medical device usage, patient information, and the system will or may provide video segments matching such query (e.g., step 406). … b. Video snippet query: User can upload (e.g., via cloud datastream 110, external devices 234) a short video snippet as query (e.g., step 402) to find situations (e.g., step 406) similar to the one in the query video. See at least [0036]-[0041]. Also: The search results may be presented to user(s) via a display and/or outputs of web user interface 112, or via a display and/or outputs of mobile user interface 114, or via display 236 and/or outputs of user interface(s) among external devices 234. See at least [0040]).
Regarding Claim 10 and 18: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses display, via a graphical user interface, the clip of the medical procedure; receive, during the display of the clip, via the graphical user interface, a query related to the medical procedure; execute the query on the embedding space to select the performance data associated with the clip; and provide a response to the query based at least in part on the performance data (Users should be able to articulate their query describing the information need, for example, a text query detailing patient description, surgical tools used, surgery phase, or video snippet query seeking similar surgical situations. See at least [0015]. Also: 4. Video retrieval: The system will or may support retrieval of surgery video or video segments using 2 types of queries: a. Text query: User can provide description of search need (e.g., step 402) including surgery type, surgery phase, medical device usage, patient information, and the system will or may provide video segments matching such query (e.g., step 406). … b. Video snippet query: User can upload (e.g., via cloud datastream 110, external devices 234) a short video snippet as query (e.g., step 402) to find situations (e.g., step 406) similar to the one in the query video. See at least [0036]-[0041]. Also: The search results may be presented to user(s) via a display and/or outputs of web user interface 112, or via a display and/or outputs of mobile user interface 114, or via display 236 and/or outputs of user interface(s) among external devices 234. See at least [0040]).
Regarding Claim 11: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses update the embedding space with a plurality of embedding vectors constructed for a plurality of clips of the video stream (This disclosure is directed to intelligent management and retrieval for videos. The videos may include medical operation videos, such as surgery videos. Some method embodiments may include a method comprising: performing a management process on each medical operation video of a plurality of medical operation videos. See at least [0004]. Also: To search in the database of surgery videos, user provided query will or may be matched against the rich description of each surgery video in the database, using natural language understanding (NLU) methods, such as sentence embedding model, e.g., USE model, BERT model. See at least [0017]. Also: USE model stands for “universal sentence encoder model,” which is a neural network model that embeds natural language sentences into a mathematical high dimensional space. The system can provide a mechanism to compare the semantic meaning of 2 or more sentences, e.g., by computing the distance between their representations (represented as vector in high dimensional space). See at least [0038]).
Regarding Claim 12: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Additionally, Kozlov teaches aggregate performance data for at least two of the plurality of clips; generate an aggregated embedding vector for the aggregated performance data; and update the embedding space with the aggregated embedding vector (generate an overall summary of the video file using these segment-specific summaries with timestamps. See at least [0027]. Also: generate a vector representation of the video by, in part, encoding the overall summary and at least a subset of frames of the video, the vector representation allowing a similarity search to be performed. See at least [0190]).
Zhao, Ayvali, and Kozlov suggest a system which searches video data based on video embeddings, upon which the claimed invention’s determination of aggregate embeddings can be seen as an improvement. However, Kozlov demonstrates that the prior art already knew of generating video embeddings based on an aggregate of descriptions and multiple video segments. One of ordinary skill in the art could have easily applied the techniques of Kozlov to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Kozlov would have resulted in an improved system which would be able to search video summaries. As such, the application of Kozlov, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, and Kozlov.
Regarding Claim 13: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao further discloses update the embedding space with a plurality of embedding vectors constructed for a plurality of clips of a plurality of video streams of a plurality of medical procedures (This disclosure is directed to intelligent management and retrieval for videos. The videos may include medical operation videos, such as surgery videos. Some method embodiments may include a method comprising: performing a management process on each medical operation video of a plurality of medical operation videos. See at least [0004]. Also: To search in the database of surgery videos, user provided query will or may be matched against the rich description of each surgery video in the database, using natural language understanding (NLU) methods, such as sentence embedding model, e.g., USE model, BERT model. See at least [0017]. Also: USE model stands for “universal sentence encoder model,” which is a neural network model that embeds natural language sentences into a mathematical high dimensional space. The system can provide a mechanism to compare the semantic meaning of 2 or more sentences, e.g., by computing the distance between their representations (represented as vector in high dimensional space). See at least [0038]).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2023/0177082 A1) in view of Ayvali et al. (US 2023/0080060 A1) and Kozlov et al. (US 2026/0094442 A1), and further in view of Kita et al. (US 2005/0086210).
Regarding Claim 7: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao does not expressly disclose executing the search query using a distance-based nearest neighbor search. However, Kita teaches executing the search query using a distance-based nearest neighbor search (When a computer performs data matching, multimedia data can be represented by a feature vector in the computer. The feature vector can be used also when data similar to a specified retrieving condition (input query) is retrieved from a database. FIG. 1 is an example showing multimedia-contents retrieval using the feature vector. When the feature vector is specified as a retrieving condition for similar retrieval, in order to perform the retrieval process, distances between a vector of the retrieving condition and vectors in the database are calculated. Then, data with a small distance are outputted as a result of the retrieval. Thus, retrieving vectors with small distances to the vector specified as the criterion from the database is referred to as a nearest neighbor search. In the nearest neighbor search, a plurality of features are represented by a multidimensional vector. The similarity of data is determined based on the distance between vectors. See at least [0004]).
Zhao, Ayvali, and Kozlov suggest a system which uses embedding of video data to retrieve videos, upon which the claimed invention’s use of a nearest neighbor search can be seen as an improvement. However, Kita demonstrates that the prior art already knew of using a nearest neighbor search on vector spaces to identify media for retrieval. One of ordinary skill in the art could have trivially applied the techniques of Kita to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Kita would have resulted in an improved system which would identify similar medical procedure videos according to a nearest neighbor criteria. As such, the application of Kita, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, Kozlov, and Kita.
Regarding Claim 8: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao does not expressly disclose executing the search query using a linear model.
However, Kita teaches executing the search query using a linear model (Linear retrieval (linear search) is known as such a retrieval of similar contents based on a feature vector. In linear retrieval, feature vectors of all data in the database are sequentially compared with the vector specified by the retrieving condition. See at least [0005]).
Zhao, Ayvali, and Kozlov suggest a system which uses embedding of video data to retrieve videos, upon which the claimed invention’s use of a linear search can be seen as an improvement. However, Kita demonstrates that the prior art already knew of using a linear search on vector spaces to identify media for retrieval. One of ordinary skill in the art could have trivially applied the techniques of Kita to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Kita would have resulted in an improved system which would identify similar medical procedure videos according to a linear search criteria. As such, the application of Kita, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, Kozlov, and Kita.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2023/0177082 A1) in view of Ayvali et al. (US 2023/0080060 A1) and Kozlov et al. (US 2026/0094442 A1), and further in view of Munoz Delgado (US 2021/0125061 A1) (hereafter referenced as Delgado).
Regarding Claim 9: Zhao in view of Ayvali and Kozlov make obvious the above limitations. Zhao does not expressly disclose executing the search query via interpolation through a generative embedding space to identify the search result, wherein the search result comprises synthetic data.
However, Delgado teaches interpolation through a generative embedding space to identify the result, wherein the result comprises synthetic data (This structure given to the latent space by the generator can be explored, e.g., by interpolating and performing vector arithmetic between points (vectors) in the latent space, and observing the (meaningful) effects on the output synthetic images. See at least [0090]).
Zhao, Ayvali, and Kozlov suggest a system which uses embedding of video data to retrieve videos, upon which the claimed invention’s use of interpretation to generate synthetic data can be seen as an improvement. However, Delago demonstrates that the prior art already knew of interpolating between vectors in a representation space to generate synthetic data. One of ordinary skill in the art could have easily applied the techniques of Delgado to the system of Zhao, Ayvali, and Kozlov. Further, one of ordinary skill in the art would have recognized that such an application of Delago would have resulted in an improved system which could provide data on cases which have not yet been recorded. As such, the application of Delago, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Zhao and the teachings of Ayvali, Kozlov, and Delago.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Ayush et al. (US 2021/0109966 A1) and Habibian et al. (US 2017/0083623 A1) provides additional disclosures regarding searching for videos based on embedding techniques.
Stoyanov et al. (US 2024/0428956 A1) specifically describes using embedding based searching to search medical surgical videos.
Lee et al. (US 2025/0190503 A1) provides additional disclosures regarding video search interfaces.
Conclusion
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-09-19
