DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/20/2026, 10/22/2026, 03/28/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 18 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 17. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
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 1-22 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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “new” in claims 1, 3, 4, 12 and 22 are used by these claims to mean “advanced” while the accepted meaning is “not existing before” The term is indefinite because the specification does not clearly redefine the term.
Claims 2-22 are also rejected by 35 USC 112(b) as are dependent from rejected base claim 1.
Appropriate correction is required.
Claim 1, 14 recites “at least one new lighting feature and/or at least one new piece of lighting information”. It is not clear that “a new piece of lighting information or a new lighting feature” is the same information or feature claimed in line 1-2 of the claim 1 earlier. This confusion renders the claim indefinite.
Claims 2-22 are also rejected by 35 USC 112(b) as are dependent from rejected base claim 1.
Appropriate correction is required.
Claim 2, line 2 recites “after step d”. It is indefinite because there is no claim limitation for step d) earlier.
Appropriate correction is required.
Claim 13, line 2 recites “after step d”. It is indefinite because there is no claim limitation for step d) earlier.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6-8, 10, 12-19, 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Green et al. (Pub. No.: US 20210070286 A1), hereafter Green.
Regarding claim 1, Green teaches a method for generating a new piece of lighting information or a new lighting feature (paragraph [0049], “The vehicle system may determine the specification information of the nearby vehicle based on one or more features (e.g., a vehicle shape, a body part, a bumper, a head light”), said method comprising
i) obtaining at least one set of training data comprising at least one piece of performance information comprising a performance recording, and at least one piece of lighting information comprising a set of control data, the piece of performance information and the piece of lighting information being assigned to each other (paragraph [0050], “In particular embodiments, the vehicle system may determine the specification information of a nearby vehicle based on the stored specification data of an associated driving behavior model. In particular embodiments, when other information about a nearby vehicle is not available, the vehicle system may generate perception of that nearby vehicle based on its specification information to understand the type and/or footprint of that nearby vehicle”);
ii) creating a machine learning model using at least one lighting feature and using at least one performance feature the at least one lighting feature and the at least one performance feature being obtained by analysis before and/or during the creating of the machine learning model, and the at least one performance feature and the at least one lighting feature being assigned to each other (paragraph [0050], “a computer vision algorithm or a machine-learning (ML) model to recognize a vehicle type or model from an image including only a part of that vehicle (e.g., a body part, a bumper, a head light, a corner, a logo, a back/side/front profile, a window, a text indicator, a sign, etc.) and determine the corresponding specification information of that particular vehicle based on the vehicle type or model”);
iii) obtaining a new piece of performance information (paragraph [0017], “vehicle performance information (e.g., all-wheel drive mode or horsepower as indicated by related text indicators), vehicle speeds, acceleration, vehicle moving paths, vehicle driving trajectories, locations, turning signal status (e.g., on-off state of turning signals), braking signal status, a distance to another vehicle, a relative speed to another vehicle, a distance to a pedestrian, a relative speed to a pedestrian, a distance to a traffic signal, a distance to an intersection, a distance to a road sign, a distance to curb, a relative position to a road line, an object in a field of view of the vehicle, positions of other traffic agents, aggressiveness metrics of other vehicles, etc”);
iv) determining at least one new lighting feature and/or at least one new piece of lighting information by inference with the machine learning model while the new piece of performance information and/or at least one new performance feature are entered, the new performance feature being obtained by analyzing the new piece of performance information (paragraph [0054], “The historical driving behavior data may indicate that the vehicle 302B had made many right turns on red without using the turning lights. The system may determine that there is a 30% chance that the vehicle 302B will turn to the right along the trajectory 352A or 352B even though the turning lights of the vehicle 302B are not flashing”).
Regarding claim 2, Green further teaches e) generating at least one lighting control command from the at least one new lighting feature (FIG. 5A,, LIGHTING CONTROLLER 522).
Regarding claim 6, Green further teaches wherein the piece of performance information and/or the new piece of performance information comprises an audio file, including an audio recording, a video file, including a video recording, at least one illustration, in particular including a sequence of illustrations, a sheet of music and/or a text (paragraph [0100], “an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these”).
Regarding claim 7, Green further teaches the piece of lighting information comprises at least one set of control data, at least one set of simulation data, at least one model, at least one text, in particular a description of concept and/or a script, at least one illustration, in particular a series of illustrations, at least one cue, at least one preset, at least one sequence, at least one stack, at least one graph, at least one plan, at least one video file, at least one description, in particular a description comprising a patch, at least one cluster and/or at least one algorithm and/or at least one data packet (paragraph [0073], “users to directly connect their devices to the data collection device 560 (e.g., to exchange data, verify identity information, provide power, etc.). In particular embodiments, the data collection device 560 may be able to issue instructions (e.g., through the connector 516 in FIG. 5C) to the vehicle's onboard computer and cause it to adjust certain vehicle configurations. In particular embodiments, the data collection device 560 may be configured to query the vehicle (e.g., through the connector 516 in FIG. 5C) for certain data, such as current configurations of any of the aforementioned features, as well as the vehicle's speed, fuel level, tire pressure, external temperature gauges, navigation systems, and any other information available through the vehicle's computing system”).
Regarding claim 8, Green further teaches the piece of performance information and/or the new piece of performance information comprises a piece of music information, and the performance feature and/or the new performance feature comprises a tempo, a beat, a rhythm, a key, a sound sequence, a pitch, a vocal range an instrument used ,a kind of use of the instrument, an arrangement, the lyrics, a frequency spectrum, an amplitude, a frequency deflection, an onset strength, an effective value, the MFCCs (mel frequency cepstral coefficients), a channel, an emotion, a musical genre, a change thereof and/or a number thereof (paragraph [0081], “Ride information may include, for example, preferred pick-up and drop-off locations, driving preferences (e.g., safety comfort level, preferred speed, rates of acceleration/deceleration, safety distance from other vehicles when travelling at various speeds, route, etc.), entertainment preferences and settings (e.g., preferred music genre or playlist, audio volume, display brightness, etc.)”).
Regarding claim 10, Green further teaches the lighting feature and/or the new lighting feature is a color of light, a hue, a color temperature, a brightness, a zoom, a focus, an iris, a shape, an the orientation, a twist, position, an arrangement, a status, a type, a mood, a number of lighting devices, a maximum value thereof, a minimum value thereof and/or a change thereof (paragraph [0077], “Information may be conveyed via, e.g., scrolling text, color, patterns, animation, and any other visual display”).
Regarding claim 12, Green further teaches wherein at least one location feature is used when creating the machine learning model in step b) and/or at least one new location feature is used when determining the at least one new lighting feature and/or the new piece of lighting information in step d), the location feature and/or the new location feature being ambient brightness, the time of day, a dimension, a seating category, a number of visitors, a kind of venue and/or an audience atmosphere (paragraph [0014]).
Regarding claim 13, Green further teaches wherein the method comprises after step d):e) evaluating the at least one new lighting feature and/or the at least one new piece of lighting information (paragraph [0017], “vehicle performance information (e.g., all-wheel drive mode or horsepower as indicated by related text indicators), vehicle speeds, acceleration, vehicle moving paths, vehicle driving trajectories, locations, turning signal status (e.g., on-off state of turning signals), braking signal status, a distance to another vehicle, a relative speed to another vehicle, a distance to a pedestrian, a relative speed to a pedestrian, a distance to a traffic signal, a distance to an intersection, a distance to a road sign, a distance to curb, a relative position to a road line, an object in a field of view of the vehicle, positions of other traffic agents, aggressiveness metrics of other vehicles, etc”).
Regarding claim 14, Green further teaches a computer system (FIG. 5a) configured to execute the method according to claim 1, said computer system comprising at least one interface(I/O INTERFACE 516) for obtaining the at least one set of training data and/or obtaining the new piece of performance information for in carrying out step a) and step c), a computer-readable storage medium (FIG. 5A, MEMORY 528) creating and/or storing the machine learning model for in carrying out step b) and step d) and at least one data processing device (FIG. 5A, PROCESSOR 518) that is configured to carry out step b) and step d).
Regarding claim 15, Green further teaches a computer program product (paragraph [0011], “the attached claims directed to a method, a storage medium, a system and a computer program product”) comprising computer accessible data storage storing therein sections of software code (FIG. 5A, a data storage module (a volatile memory 528, a non-volatile storage 520) that are configured so that at least one processor (FIG. 5A, PROCESSOR 518) executes a method according to claim 1.
Regarding claim 16, Green further teaches wherein the piece of performance information and/or the new piece of performance information comprises an audio recording, a video recording, or a sequence of illustrations (paragraph [0088], “entertainment content (e.g., music, video, and news) ride requestor information, ride information, and any other suitable information. Examples of data transmitted from the autonomous vehicle 640 may include, e.g., telemetry and sensor data”).
Regarding claim 17, Green further teaches the piece of lighting information includes a control code and/or a packet of control data, a virtual 3D model, a text description of concept and/or a script, a series of illustrations, a series of cues, a graph with position information and/or movement information, at least one plan, a layout plan, a video recording and/or a simulation, a description comprising a patch, at least one data packet comprising recipes, MAtricks, phasers, timecodes, macros, Lua plugins, filters, selections, effects, bitmaps and/or generators (paragraph [0088], “Example of received data may include, e.g., instructions, new software or software updates, maps, 3D models, trained or untrained machine-learning models, location information (e.g., location of the ride requestor, the autonomous vehicle 640 itself, other autonomous vehicles 640, and target destinations such as service centers), navigation information, traffic information, weather information, entertainment content (e.g., music, video, and news) ride requestor information, ride information, and any other suitable information. Examples of data transmitted from the autonomous vehicle 640 may include, e.g., telemetry and sensor data, determinations/decisions based on such data, vehicle condition or state (e.g., battery/fuel level, tire and brake conditions, sensor condition, speed, odometer, etc.)”).
Regarding claim 18, Green further teaches a control code and/or a packet of control data, a virtual 3D model, a description of concept and/or a script, a series of illustrations, a series of cues, a graph with position information and/or movement information, a layout plan, a video recording and/or a simulation, a description comprising a patch, and/or at least one data packet comprising recipes, MAtricks, phasers, timecodes, macros, Lua plugins, filters, selections, effects, bitmaps and/or generator (paragraph [0088], “Example of received data may include, e.g., instructions, new software or software updates, maps, 3D models, trained or untrained machine-learning models, location information (e.g., location of the ride requestor, the autonomous vehicle 640 itself, other autonomous vehicles 640, and target destinations such as service centers), navigation information, traffic information, weather information, entertainment content (e.g., music, video, and news) ride requestor information, ride information, and any other suitable information. Examples of data transmitted from the autonomous vehicle 640 may include, e.g., telemetry and sensor data, determinations/decisions based on such data, vehicle condition or state (e.g., battery/fuel level, tire and brake conditions, sensor condition, speed, odometer, etc.)”).
Regarding claim 19, Green further teaches herein the piece of performance information and/or the new piece of performance information comprises a piece of music information including a music recording, and the performance feature and/or the new performance feature comprises an effective value of the frequency, MFCCs (mel frequency cepstral coefficients), a channel, a direction assigned to a channel (paragraph [0081], “Ride information may include, for example, preferred pick-up and drop-off locations, driving preferences (e.g., safety comfort level, preferred speed, rates of acceleration/deceleration, safety distance from other vehicles when travelling at various speeds, route, etc.), entertainment preferences and settings (e.g., preferred music genre or playlist, audio volume, display brightness, etc.)”).
Regarding claim 22, Green further teaches wherein at least one location feature is used when creating the machine learning model in step b) and/or at least one new location feature is used when determining the at least one new lighting feature and/or the new piece of lighting information in step d), the location feature and/or the new location feature being ambient brightness, time of day, a dimension of a stage and/or an audience area, a seating category, a number of visitors, a kind of venue and/or an audience atmosphere (paragraph [0014]).
Allowable Subject Matter
Claims 3-5, 9, 11, 20, 21 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to Claim 1, comprising “the new performance information comprises a new time component, wherein a new value of the new time component is assigned and/or has been assigned to the new performance feature in step d), and wherein the determination of the at least one new lighting feature and/or the at least one new piece of lighting information by inference with the machine learning model takes is based at least in part on the new value of the new time component”, as required in combination with the other limitations of the claim.
Dependent claim 4 is also objected by virtue of its dependency.
Regarding claim 5, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to claim 1, comprising “wherein, in an n-th stage of the analysis, which is not the first stage of the analysis, at least one performance feature of the n-th stage is obtained from the piece of performance information and/or from at least one performance feature of a lower stage, at least one new performance feature of the n-th stage is obtained from the new piece of performance information and/or from at least one new performance feature of a lower stage, and/or a lighting feature of the n-th stage is obtained directly from the piece of lighting information and/or from at least one lighting feature of a lower stage”, as required in combination with the other limitations of the claim.
Regarding claim 9, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to claim 1, comprising “in step b) and/or the analysis of the new piece of performance information in step d) comprises the creation of a chromogram, a spectrogram, a periodogram, a similarity matrix, a distribution of frequency of changes, a correlation measure, a novelty function, a beat track and/or a separation of melody and percussion sources”, as required in combination with the other limitations of the claim.
Regarding claim 11, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to claim 1, comprising “wherein the analysis of the piece of lighting information in step b) comprises the creation of a cluster, an arrangement, a simulation, a similarity matrix and/or a distribution of frequency of changes”, as required in combination with the other limitations of the claim.
Regarding claim 20, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to claim 1, comprising “step d) comprises creation of a a constant-Q chromogram or an STFT chromogram, a tempo gram, a constant-Q spectrogram or an STFT spectrogram, a periodogram, a similarity matrix, a distribution of frequency of changes, a correlation measure, a novelty function, a beat track and/or a separation of melody and percussion sources”, as required in combination with the other limitations of the claim.
Regarding claim 21, prior arts whether stand alone or in combination fail to teach or reasonably suggest the method according to claim 1, comprising “the analysis of the piece of lighting information in step b) comprises the creation of a cluster of lighting devices on a basis of type, orientation and/or position, an arrangement, a simulation, a similarity matrix and/or a distribution of frequency of changes”, as required in combination with the other limitations of the claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED M KAISER whose telephone number is (571)272-9612. The examiner can normally be reached M-F 9 a.m.-6 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Riyami can be reached at 571-270-3119. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED M KAISER/ Examiner, Art Unit 2831 /ABDULLAH A RIYAMI/Supervisory Patent Examiner, Art Unit 2831
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