CTNF 18/919,996 CTNF 95830 DETAILED ACTION This office action is in response to Applicant’s submission filed on 10/18/2024. Claims 1-20 are pending in the application of which Claims 1, 12, and 17 are independent and have been examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement(s)(IDS) submitted on 10/18/2024, and 3/4/2026 have been considered by the examiner. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1, 5, 9, 12, 15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen et al. (US20220188356A1)(herein " Jorgensen"), and in further view of Plumley et al. (US20250036607A1)(herein " Plumley") . Regarding claims 1, 12, and 17 Jorgensen teaches [ A computer-implemented method comprising :- claim 1], [ A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device : - claim 12], and [ An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured : - claim 17] (Jorgensen teaches a computer-implemented method comprising computer implemented method in an apparatus comprising processor and non-transitory computer readable storage medium, par. 0005-0009, 0104-0109). obtaining a plurality of portions of at least one digital file ; (Jorgensen, Par. 0033:” … graph generator 106 may receive tracks generated by the multi-object tracking engine 104 and build one or more spatiotemporal proximity graphs (e.g., spatiotemporal proximity graph 110) based on the received tracks .”) determining one or more spatial relationships and one or more sequential relationships associated with the plurality of portions within the at least one digital file ; (Jorgensen, Fig. 1, par. 0032-0034, determining spatial relationships by generating one or more spatiotemporal proximity graphs, and Par. 0033:” According to some embodiments, a graph generator 106 may receive tracks generated by the multi-object tracking engine 104 and build one or more spatiotemporal proximity graphs (e.g., spatiotemporal proximity graph 110) based on the received tracks . For example, the graph generator 106 may use the tracks to automatically build a spatiotemporal proximity graph (e.g., spatiotemporal proximity graph 110) including one or more nodes. Moreover, each node may represent a distinct tracked entity (e.g., a specific truck) and the edges between nodes may represent a proximity relationship or a significant period of close spatial proximity between two entities. According to some embodiments, nodes may also have one or more “active” edges (e.g., self-loops) with attributes indicating a start and end time of each interval in which a corresponding entity was present in the scene.”, and Fig. 1, Par. 0030:”… receive one or more video frame (s) 102, e.g., archival footage or live video streams .”) generating at least one graph representation of at least portions of the at least one digital file based at least in part on the one or more determined spatial relationships and the one or more determined sequential relationships ; (Jorgensen, Fig. 1-2, par. 0032-0034, 0042-0043, generating a graph representing sequential relationship and spatial relationship such as spatiotemporal proximity graph 220, such as Par. 0042:” FIG. 2 is a diagram illustrating a physical environment 200 including a display 210. The display 210 may present (e.g., via a graphic user interface) a spatiotemporal proximity graph 220 according to an aspect of the application. According to some embodiments, spatiotemporal proximity graph 220 may be a directed, attributed multigraph. Moreover, spatiotemporal proximity graph 220 may include one or more nodes representing distinct tracked entities (e.g., a specific person, object, or vehicle). The edges between entities may represent a proximity relationship or a significant period of close spatial proximity that is expressed by an edge attribute as a range of contiguous frame numbers .”) encoding one or more portions of the at least one graph representation using one or more artificial intelligence techniques ; (Jorgensen, Fig. 1, par.0023-0026, 0037-0040, spatial-temporal transformers such as activity extractor 112 encoding graph representation such as spatiotemporal proximity graph 110 to generate high-level activity graph, Par. 0040:” According to some embodiments, the activity extractor 112 may employ machine learning [ artificial intelligence techniques ] based classifiers (e.g., a three dimensional convolutional neural networks) to extract activities that are not otherwise detectable from the graph structure. For example, the activity extractor 112 may employ a machine learning classifier to partition a node's “active edge” into different unary activities for personnel nodes (e.g., walking, running, lying prone, and gesturing ) or for vehicle nodes (e.g., turning left, turning right, making a U-turn, moving, or stationary ). In some embodiments, the activity extractor may extract the cropped video sequence representing the interaction for proximity edges that are not otherwise part of a detected activity and classify it using machine learning. For example, proximity edges that are not otherwise part of a detected activity may be defined by the bounding boxes of the participating tracks and it may be determined that the proximity edge between two personnel nodes represents two individuals fighting, talking or shaking hands .”) aggregating at least a plurality of the one or more encoded portions of the at least one graph representation into a spatial-temporal feature representation of the at least one digital file ; and (Jorgensen, Fig. 1, Par. 0039, aggregating a plurality of encoded portions to generating a spatial-temporal feature such as high-level activity is identified based on combination of activity edges, such as Par. 0039:” According to some embodiments, the activity extractor 112 may generate a high-level activity graph (e.g., high-level activity graph 116). In some embodiments, detected activities may be represented in the high-level activity graph by “activity edges” between nodes participating in the activities. According to some embodiments, construction of the activity graph (e.g., an activity detection process), may be performed by the activity extractor 112 in an incremental manner. Moreover, some complex activities may be detected as a combination of previously added activity edges and underlying proximity edges. For example, the activity “loading an object into a vehicle” may be defined as an object entering a vehicle (e.g., represented as an “entered” activity edge between an object node and a vehicle node) and a proximity edge between a person and the object node with overlapping temporal attributes .”) [Claim 1 only] wherein the method is performed by at least one processing device comprising a processor coupled to a memory . (Jorgensen, teaches computer implemented method in an apparatus comprising processor and non-transitory computer readable storage medium, par. 0005-0009, 0104-0109. Jorgensen, does not teach, however, Plumley teaches performing similarity detection for the at least one digital file relative to one or more additional digital files based at least in part on the spatial-temporal feature representation of the at least one digital file ; ( Plumley, Par. 0088:” A Jaccard distance operation may determine a similarity metric of two sets of (encoded) spacetime hashes in the roaring bitmap. For instance, if two devices (e.g., with different device IDs) have spacetime hashes stored as bit integers in the roaring bitmap, the server 120 may execute the Jaccard distance operation and obtain a similarity metric between two devices (based on the observations encoded in the roaring bitmap) and quantify the relationship between these entities. For instance, to quantify the relationship , the server 120 may determine the two devices are the same or co-travelers if the two sets have a similarity metric above a threshold value, or determine the two devices are not the same or co-travelers if the similarity metric is below a threshold value.”, and Par. 0137:” In order to perform event correlation between incoming data and records existing at the datastore 125, determining matching records based on Table 2 may be computationally intensive (e.g., in time and compute). Instead, the server 120 may determine similarity of topic hashes of such records .”) Plumley is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen further in view of Plumley to perform similarity detection for the at least one digital file relative to one or more additional digital files based at least in part on the spatial-temporal feature representation of the at least one digital file. Motivation to do so would improve the efficiency and accuracy of the event tracking process (Plumley, Par. 0164). Regarding claims 5, 15, and 20 , Jorgensen, as modified above, teaches the method, the storage medium, and the apparatus claims of 1, 12, and 17 respectively. Jorgensen, as modified above, further teaches wherein aggregating at least a plurality of the one or more encoded portions of the at least one graph representation comprises processing at least a plurality of the one or more encoded portions of the at least one graph representation using one or more graph pooling operations . ( Jorgensen, Fig. 1, Par. 0039, aggregating, using one or more graph pooling operations, the at least one graph representation such as high-level activity is identified based on combination of activity edges, such as Par. 0039:” According to some embodiments, the activity extractor 112 may generate a high-level activity graph (e.g., high-level activity graph 116). In some embodiments, detected activities may be represented in the high-level activity graph by “activity edges” between nodes participating in the activities. According to some embodiments, construction of the activity graph (e.g., an activity detection process), may be performed by the activity extractor 112 in an incremental manner. Moreover, some complex activities may be detected as a combination of previously added activity edges and underlying proximity edges. For example, the activity “loading an object into a vehicle” may be defined as an object entering a vehicle (e.g., represented as an “entered” activity edge between an object node and a vehicle node) and a proximity edge between a person and the object node with overlapping temporal attributes .”). Regarding claim 9, Jorgensen, as modified above, teaches the method claim of 1. Jorgensen, as modified above, does not teach, however, Plumley further teaches dividing the at least one digital file into the plurality of portions ; and (Plumley, Par. 0009:” … obtaining event information for a plurality of events , where the event information includes time-and-geolocation data indicating a setting of an event, and topic data indicating a subject of the event . “, and Par. 0014:” … processing data from records associated with the at least one matching keys using a narrative generation model to generate the event content.”, and Par. 0030:” … the systems may encode the plurality of events /observations/records into a plurality of hashes and search the index for matching hashes.”) hashing the plurality of portions of the at least one digital file . (Plumley, Par. 0009:” … The operations further include generating a plurality of compound hashes based on the plurality of events , where each compound hash includes a spacetime hash and a topic hash for a specific event.”, and Par. 0011:” … executing a query of the search data structure based on a query compound hash and/or query parameters. This includes comparing a query spacetime hash with the plurality of spacetime hashes recorded within the search data structure and comparing a query topic hash with the plurality of topic hashes recorded within the search data structure.”, and Par. 0030:” … the systems may encode the plurality of events /observations/records into a plurality of hashes and search the index for matching hashes.”) 07-21-aia AIA Claim s 2, 8, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen and Plumley, and in further view of Steadman et al. (“kD-STR: A Method for Spatio-Temporal Data Reduction and Modelling”, published May 2021)(herein " Steadman ") . Regarding claims 2, 13, and 18, Jorgensen, as modified above, teaches the method, the storage medium, and the apparatus claims of 1, 12, and 17 respectively. Jorgensen, as modified above, does not teach, however, Steadman teaches wherein determining one or more spatial relationships and one or more sequential relationships associated with the plurality of portions within the at least one digital file comprises generating one or more sequential-aware k-dimensional trees representing one or more of the plurality of portions of the at least one digital file . ( Steadman, Fig. 3, Section 4, and section 4.1:” After the clustering tree is formed for the feature space, regions in the spatio-temporal T x S D space are found for each level of the cluster tree . For a given level of the tree, each instance in the T x S D space is labelled with the cluster it has been grouped into. Then, homogeneous regions that are connected components belonging to the same cluster in the T x S D space are found. Since storing the bounding spatio-temporal polygon of each homogeneous region may require many coordinates to be stored, we assert that each region must be defined by a single start and end time to limit the shapes in the T x S D space that regions may take .”) PNG media_image1.png 240 676 media_image1.png Greyscale Steadman is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Steadman to generate one or more sequential-aware k-dimensional trees representing one or more of the plurality of portions of the at least one digital file. Motivation to do so would reduce the volume of data to be processed whilst minimizing the error introduced in later analysis or modelling (Steadman, Section 1 Introduction). Regarding claim 8, Jorgensen, as modified above, teaches the method claim of 1. Jorgensen, as modified above, does not teach, however, Steadman teaches wherein aggregating at least a plurality of the one or more encoded portions of the at least one graph representation into a spatial-temporal feature representation of the at least one digital file comprises aggregating at least a plurality of the one or more encoded portions of the at least one graph representation into a vector representing one or more features of the at least one digital file. ( Steadman, Section 3:” Each instance d t,s is a vector of values over the set of features F, i.e., d t,s = ( d 1 t,s , ... , d |F| t,s ). For example, in a weather dataset these features may be rainfall, temperature, and humidity. For generality, we assume that these features are real-valued. Therefore, the dataset is a mapping from the k -dimensional spatio-temporal space to the |F|-dimensional feature space, D : T x s D ➔ ~ R IFI . Techniques exist for representing binary and categorical features as real-valued data, and appropriate partitioning algorithms can be used for binary and categorical features.”) Steadman is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Steadman to aggregate at least a plurality of the one or more encoded portions of the at least one graph representation into a vector representing one or more features of the at least one digital file. Motivation to do so would reduce the volume of data to be processed whilst minimizing the error introduced in later analysis or modelling (Steadman, Section 1 Introduction) . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen, Plumley, and Steadman, and in further view of Shah et al. (US20210185066A1)(herein "Shah") . Regarding claim 3, Jorgensen, as modified above, teaches the method claim of 2. Jorgensen, as modified above, does not teach, however, Shah teaches wherein generating one or more sequential-aware k-dimensional trees comprises encoding spatial information of the plurality of portions of the at least one digital file using at least one dynamic position technique, and incorporating context information into the plurality of portions of the at least one digital file using at least one file variational autoencoder. (Shah, Par. 0017:” As an option, the variational autoencoder neural network structure includes an encoding neural network structure and a decoding neural network structure, where: the encoding neural network structure is trained and configured to generate an N-dimensional vector by parsing the tree graph associated with the application message by accumulating one or more context vectors associated with nodes of the tree graph , wherein a context vector for a parent node of the tree graph is based on values representative of information content of the parent's child node (s); and the decoding neural network structure is trained and configured to generate a tree graph based on an N-dimensional vector associated with the application message in a recursive approach based on generating nodes of the tree graph and context information from the N-dimensional vector for each of the generated nodes of the tree graph based on modelling relationships between parent nodes and child node (s) and relationships between child node(s) of the same parent node of the tree graph .”) Shah is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Shah to encode spatial information of the plurality of portions of the at least one digital file using at least one dynamic position technique, and incorporating context information into the plurality of portions of the at least one digital file using at least one file variational autoencoder. Motivation to do so would improve upon the detection of anomalous application messages (Shah, Par. 0068) . 07-21-aia AIA Claim s 4, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen and Plumley, and in further view of Fan et al. (US20250103884A1)(herein "Fan") . Regarding claims 4, 14, and 19, Jorgensen, as modified above, teaches the method, the storage medium, and the apparatus claims of 1, 12, and 17 respectively. Jorgensen, as modified above, does not teach, however, Fan teaches wherein encoding one or more portions of the at least one graph representation comprises processing one or more portions of the at least one graph representation using at least one graph neural network in connection with one or more spatial-temporal transformers . (Fan, Fig. 3-5, par. 0095, 0131, using graph neural network 504 with spatial-temporal transformers such as spatial-temporal encoder 501, such as Par. 0131:” In some non-limiting embodiments or aspects, spatial-temporal encoder 501 may employ one or more machine learning models, such as neural network models, including, but not limited to, a GNN 504, a RNN 506, a CNN 508, and/or the like. In some non-limiting embodiments or aspects, predictor 503 may employ one or more machine learning models, such as MLP 510.”) Fan is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Fan to process one or more portions of the at least one graph representation using at least one graph neural network in connection with one or more spatial-temporal transformers. Motivation to do so would more accurately predict future multivariate time-series data, while minimizing computer resources required to achieve such levels of prediction accuracy … (Fan, Par. 0002-0004) . 07-21-aia AIA Claim s 6, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen and Plumley, and in further view of Zhang et al. (US 20250328523A1)(herein "Zhang") . Regarding claims 6, and 16, Jorgensen, as modified above, teaches the method, and the storage medium claims of 1, and 12 respectively. Jorgensen, as modified above, does not teach, however, Zhang teaches wherein performing similarity detection for the at least one digital file relative to one or more additional digital files comprises processing the spatial-temporal feature representation of the at least one digital file against one or more spatial-temporal feature representations attributed to the one or more additional digital files using one or more machine learning-based fuzzy matching techniques . (Zhang, Par. 0041:” … each of the active components of the system 100 perform their functionality at runtime or after a machine learning model has been deployed.”, and Par. 0058:” … the geocoder 124 may first programmatically call the spatial/temporal constraint detector 106 to retrieve each geographic location entity. For example, the spatial/temporal constraint detector 106 may first break a natural language command down into its individual components, such as street name, city, state, postal code, and country. … The geocoder 124 may then attempts to match the parsed address against the data in its database to find a corresponding geographic location. It may use various matching algorithms (e.g., via fuzzy matching or spatial indexing, such as R-trees) to find the best match, taking into account factors such as spelling variations, abbreviations, and proximity to known landmarks.”) Zhang is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Zhang to process the spatial-temporal feature representation of the at least one digital file against one or more spatial-temporal feature representations attributed to the one or more additional digital files using one or more machine learning-based fuzzy matching techniques. Motivation to do so would improve the user experience. (Zhang, Par. 0036) . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen and Plumley, and in further view of Lida Huang (US 8942515B1)(herein "Huang") . Regarding claim 7, Jorgensen, as modified above, teaches the method claim of 1. Jorgensen, as modified above, does not teach, however, Huang teaches wherein performing similarity detection for the at least one digital file relative to one or more additional digital files comprises detecting similarities with respect to digital file content and at least one of digital file content structure and digital file content sequence . (Huang, Col. 7, line 54- Col. 8, line 3:” … the topological relationship in each dimension between the feature points to identify the input image in an image database 450 may include steps of building a predetermined data structure in the image database 4501; providing the key features from the input image 4502; retrieving the key features (from the input image) in the predetermined data structure within a given range 4503; comparing the sequence (s) between the input images and possible matching images in the image database to generate one or more TOD scores 4504; and calculating and sorting the TOD scores to retrieve matching images in the image database 4505. In one embodiment, the predetermined data structure can be a k-dimensional tree ("k-d tree"), which is a space-partitioning data structure for organizing points in a k-dimensional space. Namely, the k-d tree is used to hierarchically decompose spaces into a plurality of cells such that it is fast to access any input object by position in each cell.”) Huang is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Huang to detect similarities with respect to digital file content and at least one of digital file content structure and digital file content sequence. Motivation to do so would provide an efficient image retrieval method and apparatus that is rotation invariant, scale invariant, and tolerant to affine and perspective transformation. (Huang, Col. 2, ll. 39-42) . 07-21-aia AIA Claim 10 is re jected under 35 U.S.C. 103 as being unpatentable over Jo rgensen and Plumley, and in further view of Jha et al. (US 20230367961 A1)(herein "Jha") . Re garding claim 10, Jorgensen, as modified above, teaches the method claim of 1. Jorgensen, as modified above, does not teach, however, Jha teaches performing one or more automated actions based at least in part on results of the similarity detection . (Jha, Par. 0081:” … performing one or more automated actions can include identifying one or more items of address information, from the stored address information, having a highest level of similarity to the one or more parsed address components upon a determination that the results comprise a matching score below a given threshold value, and/or outputting a notification indicating acceptance of the one or more parsed address components upon a determination that the results comprise a matching score above a given threshold value.”) Jha is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Jha to perform one or more automated actions based at least in part on results of the similarity detection. Motivation to do so would rapidly trigger relevant responses—such as routing tasks, flagging duplicates, or providing personalized recommendations—without human intervention, which drastically reduces manual effort and increases processing efficiency . 07-21-aia AIA Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jorgensen and Plumley, and in further view of Ogura et al. (US 20230367961 A1)(herein " Ogura ") . Regarding claim 11, Jorgensen, as modified above, teaches the method claim of 10. Jorgensen, as modified above, does not teach, however, Ogura teaches wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on the results of the similarity detection. (Ogura, Par. 0017:” … For example, language processing system 102 may apply an artificial intelligence natural language processing technique, such as Word2vec, fastText, GloVe, vector space modeling, normalized compression distance determination, or feature learning, among other examples, …”, and Par. 0036:” … In this case, language processing system 102 may automatically recommend training to improve skills in skill clusters for which a similarity score does not satisfy a threshold.”) Ogura is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jorgensen, as modified above, further in view of Ogura to automatically training at least a portion of the one or more artificial intelligence techniques based at least in part on the results of the similarity detection. Motivation to do so would provide continuous, self-correcting improvement without manual intervention, where the AI can automatically identify anomalies, refine its own accuracy, and quickly update its training parameters to adapt to real-world changes . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Sadowski et al.( SimHash: Hash-based Similarity Detection) teaches in Abstract:” Most hash functions are used to separate and obscure data, so that similar data hashes to very different keys. We propose to use hash functions for the opposite purpose: to detect similarities between data. Detecting similar files and classifying documents is a well-studied problem, but typically involves complex heuristics and/ or O(n2) pair-wise comparisons. Using a hash function that hashed similar files to similar values, file similarity could be determined simply by comparing pre-sorted hash key values. The challenge is to find a similarity hash that minimizes false positives. We have implemented a family of similarity hash functions with this intent. We have further enhanced their performance by storing the auxiliary data used to compute om hash keys. This data is used as a second filter after a hash key comparison indicates that two files are potentially similar. We use these tests to explore the notion of "similarity.” Examiner's Note: Examiner has cited particular columns and line numbers and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARIOUSH AGAHI whose telephone number is (408)918-7689. The examiner can normally be reached Monday - Thursday and alternate Fridays, 7:30-4:30 PT. 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, Bhavesh Mehta can be reached on 571-272-7453. 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. DARIOUSH AGAHI, P.E. Primary Examiner /DARIOUSH AGAHI/Primary Examiner, Art Unit 2656 Application/Control Number: 18/919,996 Page 2 Art Unit: 2656
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