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 .
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.
Claim(s) 1-7, 9, 11, 13-18, 20-22, 25-27, and 29-30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ozcan et al. (U.S. PGPUB 20210142170).
With respect to claim 1, Ozcan et al. disclose a system for the display or projection of high-resolution images (paragraph 108, FIG. 9 illustrates schematically an embodiment of a hybrid optical and electronic neural network-based neural network system 40 according to one embodiment) comprising:
at least one electronic encoder network (first substrate layer 16 of D²NN 10) comprising a trained deep neural network (trained diffractive deep neural network DNN) configured to receive one or more high-resolution images (training images or training optical signals) and generating low-resolution modulation patterns or images (phase and/or amplitude modulated images) representative of the one or more high-resolution images (training images optical signals) using one of: a display, a projector, a screen, a spatial light modulator (SLM), or a wavefront modulator (optical modulator, reflection, and transmission-based modulator devices, paragraph 104, non-passive components may be incorporated in into the substrates 16 such as spatial light modulators (SLMs). SLMs are devices that imposes spatial varying modulation of the phase, amplitude, or polarization of a light); and
an all-optical decoder network (all-optical Diffractive Deep Neural Network D²NN 10) comprising one or more optically transmissive (transmissive material) and/or reflective (optically reflective material) substrate layers (substrate layers 16) arranged in an optical path (optical path 11 in Fig. 1), each of the optically transmissive and/or reflective substrate layer(s) comprising a plurality of physical features (physical features 18/3D-printed neuron 24 in Fig. 3) formed on or within the one or more optically transmissive and/or reflective substrate layers and having different transmission and/or reflective properties (different complex-valued transmission coefficients) as a function of local coordinates (function of lateral coordinates) across each substrate layer (paragraph 96, The D²NN 10 contains a plurality of substrates 16 that form layers (referred to herein sometimes as substrate layers 16) which may be formed as a physical substrate or matrix of optically transmissive material (for transmission mode) or optically reflective material (for reflective mode one or more materials in the D.sup.2NN 10 form a reflective surface)), wherein the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features receive light resulting from the low resolution modulation patterns or images (phase and/or amplitude modulated images) representative of the one or more high-resolution images (training images optical signals, paragraph 122, D²NN trained for handwritten digit classification) and optically generate corresponding high-resolution image projections (output optical image/signal 22 in Fig. 1) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 1).
With respect to claim 2, Ozcan et al. disclose the system of claim 1, wherein the low-resolution modulation patterns or images comprise phase-only modulation, amplitude-only modulation, or complex-valued modulation (paragraph 126, for a coherently illuminated D²NN 10 one can use the amplitude and/or phase channels of the input plane 20 to represent data to be classified. In the digit classification results reported earlier, input objects were encoded using the amplitude channel, and to demonstrate the utility of the phase channel of the network input, each input image was encoded corresponding to a fashion product as a phase-only object modulation).
With respect to claim 3, Ozcan et al. disclose the system of claim 1, wherein the trained deep neural network comprises a trained convolutional neural network (CNN) (paragraph 204, a single fully-connected (FC) digital layer and a custom designed 4-layer convolutional neural network (CNN) (referred to it as 2C2F-1 due to the use of 2 convolutional layers with a single feature and subsequent 2 FC layers) represent the lower end of the spectrum).
With respect to claim 4, Ozcan et al. disclose the system of claim 1, wherein the trained deep neural network and the plurality of physical features formed on or within the one or more optically transmissive and/or reflective substrate layers are jointly trained (paragraph 229, In the second stage of the training process, the already trained 5-layer D²NN optical front-end 42 (preceding the detector array 26) was cascaded and jointly-trained with a digital neural network 44).
With respect to claim 5, Ozcan et al. disclose the system of claim 1, wherein the all-optical decoder network comprises a single optically transmissive substrate layer or a single reflective substrate layer (paragraph 18, FIG. 3 illustrates a single substrate layer of a D²NN).
With respect to claim 6, Ozcan et al. disclose the system of claim 1, wherein the low-resolution modulation patterns or images comprise one of the following wavelengths: ultra-violet wavelengths, visible wavelengths, infrared wavelengths, or THz wavelengths (paragraph 97, The source of light 12 that illuminates the object 14 may have any number of wavelengths including visible light (e.g., light with a wavelength in the range of about 380 nm to about 740 nm) as well as light outside the perception range of humans. For example, the wavelength operating range may extend beyond the visible perception range of humans (e.g., from about 300 nm to about 1,000 nm)).
With respect to claim 7, Ozcan et al. disclose the system of claim 1, wherein the generated high-resolution image projections at the output field-of-view exhibit color information of the corresponding images (paragraph 157, It should be noted that color image data can also be applied to D²NN framework although a single wavelength THz system was used for testing. For colorful images, as an example, Red, Green and Blue channels of an image can be used as separate parallel input planes 20 to a diffractive neural network 10).
With respect to claim 9, Ozcan et al. disclose the system of claim 1, wherein one or more detectors (optical sensors 26), an observation plane (output optical plane), a surface (surface of a sensor array), or an eye are located at the output field-of-view (output optical image or output optical signal 22/detector plane, paragraph 101, the output optical image or output optical signal 22 is captured by one or more optical sensors 26. The one or more optical sensors 26 may be coupled to a computing device 27 as noted or integrated into a device such as a camera as noted above).
With respect to claim 11, Ozcan et al. disclose a device (paragraph 108, FIG. 9 illustrates schematically an embodiment of a hybrid optical and electronic neural network-based neural network system 40 according to one embodiment) for decoding high-resolution images (training images optical signals) from low-resolution modulation patterns or images (phase and/or amplitude modulated images) representative of the one or more high-resolution images (paragraph 178, This embodiment includes an all-optical D²NN front-end 42 and a digital or electronic trained neural network back-end 44) comprising:
an all-optical decoder network (all-optical Diffractive Deep Neural Network D²NN) comprising one or more optically transmissive and/or reflective substrate layers (substrate layers 16) arranged in an optical path (optical path 11 in Fig. 1), each of the optically transmissive and/or reflective substrate layer(s) comprising a plurality of physical features (physical features 18/3D-printed neuron 24 in Fig. 3) formed on or within the one or more optically transmissive and/or reflective substrate layers (substrate layers 16) and having different transmission and/or reflective properties (different complex-valued transmission coefficients) as a function of local coordinates (function of lateral coordinates) across each substrate layer (paragraph 96, The D²NN 10 contains a plurality of substrates 16 that form layers (referred to herein sometimes as substrate layers 16) which may be formed as a physical substrate or matrix of optically transmissive material (for transmission mode) or optically reflective material (for reflective mode one or more materials in the D²NN 10 form a reflective surface)), wherein the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features receive the low resolution modulation patterns or images (phase and/or amplitude modulated images) representative of the one or more high-resolution images (training images optical signals, paragraph 122, D²NN trained for handwritten digit classification) and optically generate corresponding high-resolution image projections (output optical image/signal 22 in Fig. 1) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 1).
With respect to claim 13, Ozcan et al. disclose a method of projecting high-resolution images over a field-of-view comprising: providing a device comprising the system of claim 1; see rationale for rejection of claim 1. Ozcan et al. disclose inputting one or more high-resolution images (training images optical signals) to the electronic encoder network (first substrate layer 16 of D²NN 10) so as to generate the low-resolution modulation patterns or images (phase and/or amplitude modulated images) representative of the one or more high-resolution images (training images optical signals) and optically generating the corresponding high-resolution image projections (output optical image/signal 22 in Fig. 1) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 1).
With respect to claim 14, Ozcan et al. disclose the method of claim 13, as executed by the system of claim 2; see rationale for rejection of claim 2.
With respect to claim 15, Ozcan et al. disclose the method of claim 13, as executed by the system of claim 3; see rationale for rejection of claim 3.
With respect to claim 16, Ozcan et al. disclose the method of claim 13, as executed by the system of claim 4; see rationale for rejection of claim 4.
With respect to claim 17, Ozcan et al. disclose the method of claim 13, as executed by the similar system of claim 9; see rationale for rejection of claim 9.
With respect to claim 18, Ozcan et al. disclose the method of claim 13, as executed by the system of claim 7; see rationale for rejection of claim 7.
With respect to claim 20, Ozcan et al. disclose a method of communicating information with one or more persons comprising: transmitting low-resolution modulation patterns or images (phase and/or amplitude modulated images) representative of one or more higher-resolution images (training images or training optical signals) containing the information using one or more of: a display, a projector, a screen, a spatial light modulator (SLM), or a wavefront modulator (optical modulator, reflection, and transmission-based modulator devices, paragraph 104, non-passive components may be incorporated in into the substrates 16 such as spatial light modulators (SLMs). SLMs are devices that imposes spatial varying modulation of the phase, amplitude, or polarization of a light), as executed by the similar system of claim 11; see rationale for rejection of claim 11.
With respect to claim 21, Ozcan et al. disclose the method of claim 20, as executed by the similar system of claim 9; see rationale for rejection of claim 9.
With respect to claim 22, Ozcan et al. disclose the method of claim 20, as executed by the system of claim 7; see rationale for rejection of claim 7.
With respect to claim 25, Ozcan et al. disclose a communication system for transmitting a message or signal in space (paragraph 108, FIG. 9 illustrates schematically an embodiment of a hybrid optical and electronic neural network-based neural network system 40 according to one embodiment) comprising:
at least one electronic encoder network (first substrate layer 16 of D²NN 10) comprising a trained deep neural network (trained diffractive deep neural network DNN) configured to receive a message or signal (input optical signal 20) and generate a phase-encoded and/or amplitude-encoded optical representation (phase and/or amplitude modulated images) of the message or signal that is transmitted along an optical path (optical path 12 in Fig. 9); and
an all-optical decoder network (all-optical Diffractive Deep Neural Network D²NN 10) comprising one or more optically transmissive and/or reflective substrate layers (substrate layers 16) arranged in the optical path with the encoder network that at least partially occluded and/or blocked with an opaque occlusion and/or a diffusive medium (via optically reflective and optically diffusive material, paragraph 96, The D²NN 10 contains a plurality of substrates 16 that form layers (referred to herein sometimes as substrate layers 16) which may be formed as a physical substrate or matrix of optically transmissive material (for transmission mode) or optically reflective material (for reflective mode one or more materials in the D²NN 10 form a reflective surface)), each of the optically transmissive and/or reflective substrate layer(s) comprising a plurality of physical features formed on or within the one or more optically transmissive and/or reflective substrate layers (paragraph 103, FIG. 5 illustrates another embodiment in which the physical features 18 are created or formed within the substrate 16) and having different transmission and/or reflective properties as a function of local coordinates across each substrate layer (paragraph 103, the substrate 16 may have a substantially uniform thickness but have different regions of the substrate 16 have different optical properties. For example, the complex-valued refractive index of the substrate layers 16 may altered by doping the substrate layers 16 with a dopant (e.g., ions or the like) to form the regions of neurons 24 in the substrate layers 16 with controlled transmission properties), wherein the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features receive secondary optical waves (reflected and diffused light, via optical path 11) scattered by the opaque occlusion and/or diffusive medium (via optically reflective and optically diffusive material) and optically generate the message or signal (output optical image/signal 22 in Fig. 9) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 9).
With respect to claim 26, Ozcan et al. disclose the communication system of claim 25, as executed by the system of claim 6; see rationale for rejection of claim 6.
With respect to claim 27, Ozcan et al. disclose a device for decoding an encoded optical message or signal (paragraph 108, FIG. 9 illustrates schematically an embodiment of a hybrid optical and electronic neural network-based neural network system 40 according to one embodiment) comprising:
an all-optical decoder network (all-optical Deep Diffractive Neural Network D²NN 10) comprising one or more optically transmissive and/or reflective substrate layers (substrate layers 16) arranged in an optical path (optical path 11) of the encoded optical message or signal that at least partially occluded and/or blocked with an opaque occlusion and/or a diffusive medium (optically reflective material and optically transmissive material, paragraph 96, The D²NN 10 contains a plurality of substrates 16 that form layers (referred to herein sometimes as substrate layers 16) which may be formed as a physical substrate or matrix of optically transmissive material (for transmission mode) or optically reflective material (for reflective mode one or more materials in the D²NN 10 form a reflective surface)), each of the optically transmissive and/or reflective substrate layer(s) comprising a plurality of physical features formed on or within the one or more optically transmissive and/or reflective substrate layers (paragraph 103, FIG. 5 illustrates another embodiment in which the physical features 18 are created or formed within the substrate 16) and having different transmission and/or reflective properties as a function of local coordinates across each substrate layer (paragraph 103, the substrate 16 may have a substantially uniform thickness but have different regions of the substrate 16 have different optical properties. For example, the complex-valued refractive index of the substrate layers 16 may altered by doping the substrate layers 16 with a dopant (e.g., ions or the like) to form the regions of neurons 24 in the substrate layers 16 with controlled transmission properties), wherein the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features receive secondary optical waves (reflected and diffused light, via optical path 11) scattered by the opaque occlusion and/or diffusive medium (via optically reflective and optically diffusive material) and optically generate the message or signal (output optical image/signal 22 in Fig. 9) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 9).
With respect to claim 29, Ozcan et al. disclose a method of transmitting a message or signal over space in the presence of an obstructing opaque occlusion and/or a diffusive medium (via optically reflective and optically diffusive material, paragraph 96, The D²NN 10 contains a plurality of substrates 16 that form layers (referred to herein sometimes as substrate layers 16) which may be formed as a physical substrate or matrix of optically transmissive material (for transmission mode) or optically reflective material (for reflective mode one or more materials in the D²NN 10 form a reflective surface)), as executed by the similar system of claim 25; see rationale for rejection of claim 25. Ozcan et al. disclose inputting one or more messages or signal (input optical signal 20) to the electronic encoder network (first substrate layer 16 of D²NN 10) so as to generate the phase-encoded and/or amplitude-encoded optical representation of the message or signal (phase and/or amplitude modulated images) and optically generating the message or signal (output optical image/signal 22 in Fig. 1) at an output field-of-view (output optical image or output optical signal 22/detector plane in Fig. 9).
With respect to claim 30, Ozcan et al. disclose the method of claim 29, as executed by the system of claim 4; see rationale for rejection of claim 4.
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.
Claim(s) 8, 10, 12, 19, 23-24, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ozcan et al. (U.S. PGPUB 20210142170) in view of Uhlig (U.S. PGPUB 20230019746).
With respect to claim 8, Ozcan et al. disclose the system of claim 1. However, Ozcan et al. do not expressly disclose the generated high-resolution image projections at the output field-of-view comprise a movie.
Uhlig, who also deals with generating an image, disclose a method wherein the generated high-resolution image projections at the output field-of-view comprise a movie (paragraph 39, the contents or video from a micro-LED or micro-OLED or ultra-high-definition display is projected into a prism and then to a thin, glass, multi-layered mirror structure to provide the user with a wide FOV and a high-resolution picture).
Ozcan et al. and Uhlig are in the same field of endeavor, namely computer graphics.
Before the effective filing date of the claimed invention, it would have been obvious to apply the method wherein the generated high-resolution image projections at the output field-of-view comprise a movie, as taught by Uhlig, to the Ozcan et al. system, because this would display alternative or different media types.
With respect to claim 10, Ozcan et al. as modified by Uhlig disclose the system of claim 1, wherein the all-optical decoder network is integrated into a wearable device, goggles, or glasses (Uhlig: paragraph 37, The present invention, as well as features and aspects thereof, is directed towards providing a wide Field of View (FOV) display system for Augmented Reality (AR) headsets and Smart Glasses applications). It would have been obvious to apply the method wherein the all-optical decoder network is integrated into a wearable device, goggles, or glasses because this would allow for hands-free operation of a display system instead of typical interfaces.
With respect to claim 12, Ozcan et al. as modified by Uhlig disclose the device of claim 11, as executed by the system of claim 10; see rationale for rejection of claim 10.
With respect to claim 19, Ozcan et al. as modified by Uhlig disclose the method of claim 13, as executed by the system of claim 8; see rationale for rejection of claim 8.
With respect to claim 23, Ozcan et al. as modified by Uhlig disclose the method of claim 20, as executed by the system of claim 8; see rationale for rejection of claim 8.
With respect to claim 24, Ozcan et al. as modified by Uhlig disclose the method of claim 20, as executed by the system of claim 10; see rationale for rejection of claim 10.
With respect to claim 28, Ozcan et al. as modified by Uhlig disclose the device of claim 27, as executed by the system of claim 10; see rationale for rejection of claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 20230060689 to Ogawa for a method of projecting high-resolution video onto a field of view.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW GUS YANG whose telephone number is (571)272-5514. The examiner can normally be reached M-F 9 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW G YANG/Primary Examiner, Art Unit 2614
7/2/26
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