DETAILED ACTION
This office action is in response to Applicant’s submission filed on 11/2/2024. Claims 1-10 are pending in the application of which Claim 1 is independent 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
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365 is acknowledged. The prior-filed application (Provisional application No. 63/595835 Filed on 11/3/2023) is acknowledged.
Information Disclosure Statement
The information disclosure statement(s)(IDS) submitted on 3/28/2025 has been considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Bohl et al. (US20200211553A1)(herein "Bohl"), and in further view of Tiwari et al. (US20230160726A1)(herein "Tiwari").
Regarding claim 1, Bohl teaches a natural language processing model configured to generate real-time alerts with actionable recommendations and potential consequences based on anomalies detected in data produced by the DFOS. (Bohl, Par. 0068:” … notify or alert the user with information about the vehicle, the environment external to the vehicle, and/or the environment inside the vehicle cabin. Various models monitor visual data and other sensor data on a continuing basis [real-time] to detect anomalies. In that regard, computer vision model 406 analyzes visual inputs, including, without limitation, camera input 428 and infrared input 430. Computer vision model 406 generates a prediction 532 based on the visual inputs. Similarly, signal processing model 408 analyzes sensor data, including, without limitation, radar input 434 and thermal sensor input 436. Signal processing model 408 generates a prediction 538 based on the sensor data. … Sensor fusion 410 combines these predictions to generate an aggregate prediction 540 based on the individual predictions.”, and Par. 0069:” … the aggregate prediction 540 exceeds the predetermined threshold level then output NLP model 412 generates a natural language notification text 542 based on the aggregate prediction 540.”, and Par. 0084:” … an NLP model to the second prediction to generate a natural language notification text segment, such as an alert or notification.”, and Claim 19:” A system, … obtains first sensor data from a first sensor included in a plurality of sensors; analyzes the first sensor data to generate a first result; … and outputs a natural language alert to the user based on the second result.”)
Bohl does not teach, however Tiwari teaches an intelligent anomaly detection system for distributed fiber optic sensing (DFOS) applications comprising: the distributed fiber optic sensing system; and (Tiwari, Par. 0004:” … providing systems and methods for estimating multi-phase fluid fractions. … first determining, by distributed acoustic sensing (DAS), … a machine learning model programmed to estimate fluid fractions of a fluid flow as a function of at least one DAS fluid flow parameter and at least one physical characteristic of the fluid flow; …”, and Par. 0037:”… Fiber optic cable 202 may extend along a generally straight path for most of its length interspersed by wrapped areas 204 in which the fiber optic cable 202 wraps around a pipe such as common outflow pipe 110. … Wrapped areas 204 can include a length of fiber wrapped on the pipe of between 220 meters and 600 meters and can have a wrapped length of between 50 cm and 110 cm.”, and Par. 0038:”The far end of fiber optic cable 202 connects to a DAS acquisition unit 210.”, and Par. 0040:”Flow of fluid in the various pipes that define the fluid pathways will apply strain and vibration to fiber optic cable 202, and in particular at the wrapped areas 204. DAS acquisition unit 210 can measure the corresponding changes to light passing through fiber optic cable 202 and determine characteristics of the fluid flow, including fluid velocity, fluid rate (fluid rate and fluid velocity falling within a broader category of “fluid speed parameters”), and speed of sound through the fluid as a continuous time series.”)
Tiwari 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 Bohl further in view of Tiwari to employ the distributed fiber optic sensing system. Motivation to do so would improve production allocations, well health checks and production optimizations (Tiwari, Par. 0065).
Regarding claim 2, Bohl, as modified above, teaches the system of claim 1.
Bohl, as modified above, further teaches wherein the actionable recommendations are context aware. (Bohl, Par. 0066:” … Output NLP model 412 generates a natural language response text 442 based on the aggregate prediction 440.”, and Par. 0069:” … output NLP model 412 generates a natural language notification text 542 based on the aggregate prediction 540.”) Note: generation of NL notification based on prediction reads on “context aware”.
Regarding claim 3, Bohl, as modified above, teaches the system of claim 2.
Bohl, as modified above, further teaches wherein the natural language processing model provides the actionable recommendations based on specific DFOS application and domain. (Bohl, Par. 0041:” Sensors 116 generate sensor data corresponding to one or more objects included in an area being monitored. For example, and without limitation, sensors 116 may include visual sensors (e.g., RGB cameras, infrared cameras, etc.), distance measurement sensors (e.g., LIDAR, radar), biological sensors (e.g., electroencephalography sensors, etc.), auditory sensors (e.g., microphones), behavior sensors (e.g., mobile usage sensors, etc.), vehicle or telematics sensors (e.g., speedometers, etc.), and/or environment sensors (e.g., thermometers, etc.). Data from these sensors may be “fused” (e.g., aggregated and analyzed together) to generate meaningful information, warnings, recommendations, etc.”) Note: sensor data corresponding to various area being monitored, reads on specific application.
Regarding claim 4, Bohl, as modified above, teaches the system of claim 3.
Bohl, as modified above, further teaches wherein the natural language processing model communicates the potential consequences of detected anomalies, raises awareness in operators of potential risks while encouraging timely intervention by the operators. (Bohl, Par. 0075:” Life threatening events 730 include, without limitation, following too close to a vehicle in front 740, approaching a red light or stop sign 742, drunk, inattentive, or drowsy driving 744, approaching pedestrian, object, or cyclist 746, and exceeding a visibility-based safety speed 746. More generally, life threatening events include any condition where the user is driving in an impaired or inattentive condition or where the vehicle is in danger of colliding with an object. In one example, modules 700 related to life threatening events 730 may analyze visual input from forward camera 712 and in-cabin camera 714 with telematics 722 sensor data to determine that the user is approaching a red light or stop sign 742 and is unlikely to stop in time. In another example, modules 700 related to life threatening events 730 may analyze visual input from in-cabin camera 714 and alcohol sensor 718 data to determine if the user is engaging in drunk or drowsy driving 744. In yet another example, modules 700 related to life threatening events 730 may analyze visual input from rear camera 716 and radar 720 data to determine if a pedestrian or cyclist 746 is approaching from behind.”) Note: detection of various life-threatening events as mention in the above recitation, reads on detection of anomalies and raising awareness of the potential risks involved.
Regarding claim 5, Bohl, as modified above, teaches the system of claim 4.
Bohl, as modified above, further teaches comprising external sensors and data sources including internet-of-things sensors and sources. (Bohl, Par. 0074:” … the modules 700 receive visual input and sensor data from various sensors 710 and perform various functions categorized into three types of events, namely, life threatening events 730, better driving experience events 750, and user-to-vehicle information retrieval events 770.”) Note: IoT sensors are sensors that gather information by using various wired (Ethernet) or wireless networks to transmit data to cloud platforms or central servers. In essence, IoT sensors are merely a variation of the sensors disclosed in Bohl.
Regarding claim 6, Bohl, as modified above, teaches the system of claim 5.
Bohl, as modified above, further teaches wherein the actionable recommendations provided by the natural language processing model are adjustable in terms of verbosity, such that a level of detail provided in the actionable recommendations range from concise summaries to comprehensive explanations. (Bohl, Par. 0008:” … continuously monitor sensor data and other data sources and, in response, to proactively notify a user of certain conditions based on this data. … access to a rich variety of sensor data and other data sources, the virtual personal assistant generates notifications with improved accuracy [concise] and thoroughness [comprehensive] relative to conventional approaches.”)
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bohl, and Tiwari, and in further view of Jameel et al. (US 20200411006 A1)(herein "Jameel").
Regarding claim 7, Bohl, as modified above, teaches the system of claim 6.
Bohl, as modified above, does not teach, however, Jameel teaches wherein the actionable recommendations provided by the natural language processing model are modified by tone to suit different audiences or situations including formal tone for management-level notifications and casual tone for field personnel. (Jameel, Par. 0019:” … Transit voice assistant brings customers new levels of ease and convenience through voice technology, including natural language understanding and automatic speech recognition. … Variations in voice tones can be used for communicating different types of transit alerts. Reach and delight more customers, where they are, through millions of voice powered devices.
Jameel 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 Bohl, as modified above, further in view of Jameel to wherein the actionable recommendations provided by the natural language processing model are modified by tone to suit different audiences or situations including formal tone for management-level notifications and casual tone for field personnel. Motivation to do so would provide a unique experience for the customer (Jameel, Par. 0004).
Regarding claim 8, Bohl, as modified above, teaches the system of claim 7.
Bohl, as modified above, does not teach, however, Jameel further teaches wherein the natural language processing model is configured to provide the actionable recommendations in multiple languages. (Jameel, Par. 0019:” … Transit voice assistant brings customers new levels of ease and convenience through voice technology, including natural language understanding and automatic speech recognition.”, and Par. 0033… voice notification alerts for delays 510, fare information 520, .... The present system also provides for access in multiple languages.”)
Regarding claim 9, Bohl, as modified above, teaches the system of claim 8.
Bohl, as modified above, further teaches incorporate multimodal data including images, audio, and video to provide additional context or information with the actionable recommendations. (Bohl, Par. 0064:” … Speech recognition 402 receives speech input 422 in the form of sound waves received via audio input device 114. Speech recognition 402 decoded the speech input 422 into request text 424 and transmits the request text 424 to input NLP model 404.”, and Par. 0066:”Computer vision model 406 analyzes visual inputs, including, without limitation, camera input 428 and infrared input 430, in view of the extracted meaning 426. Computer vision model 406 generates a prediction 432 based on the visual inputs and the extracted meaning 426.”, and Par 0071:” … natural language processing 612 generates natural language words 632 based on the visual input and sensor data. … Recognition 618 generates identifiers 638 of people recognized within an image or other visual data. Body detection 620 generates position and location of bodies of people within an image or other visual data.”)
Regarding claim 10, Bohl, as modified above, teaches the system of claim 9.
Bohl, as modified above, further teaches interactively communicate between users such that users may request more information, provide feedback, or take actions directly from the natural language processing model generated actionable recommendations. (Bohl, Par. 0057:” … when sensor data from one user is combined with sensor data from multiple users to generate a multiuser machine learning model that all users may access. Visual input and sensor data may then be employed to train a multiuser machine learning model without compromising the privacy of individual users.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Sharma et al. (US20220364943A1) teaches in Par. 0004:” Distributed fiber optics sensing (DFOS) is a non-invasive, real-time sensing technology that can overcome many limitations of the traditional gauges. Among other things, fiber optic sensors are insensitive to electromagnetic interference, resistant to corrosion and high pressure and high temperature conditions and do not require any electronics along the optical path, making them suitable for many downhole sensing applications. The optical fiber functions both as the sensor and the channel to transmit the data, providing a truly distributed measurement simultaneously along the entire cable. These sensors are capable of measuring physical properties such as temperature (via Distributed Temperature Sensing or DTS), vibration (via Distributed Acoustic Sensing or DAS), and strain (via Distributed Strain Sensing or DSS), simultaneously along the entire fiber.”
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.
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DARIOUSH AGAHI, P.E.
Primary Examiner
/DARIOUSH AGAHI/Primary Examiner, Art Unit 2656
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