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WO-2026096961-A1 - APPROACHES TO DETERMINING A TARGET MATERIAL VIA IDENTIFICATION AND ANALYSIS OF SPECTRAL INFORMATION USING NON-OCCLUDED ILLUMINATION

WO2026096961A1WO 2026096961 A1WO2026096961 A1WO 2026096961A1WO-2026096961-A1

Abstract

Introduced here are approaches that allow for usable, real-world reflectance spectroscopy to be achieved using a mobile device – like a smartphone – without any additional external hardware, in the presence of ambient light. These approaches enable spectroscopic measurements of material properties, as well as the mapping and imaging of material properties (and importantly, the ability to track changes in material properties over time in a quantifiable manner). The application of these approaches are widespread, including precise determination of color; identification of skin and hair chromophore and condition; identification of material; identification of changes in material state (e.g., due to sunlight, moisture, aging, use, etc.); detection of counterfeit articles; verification of living bodies (e.g., for security purposes); authentication of documentation; and agriculture, for example, to identify plant materials and establish condition.

Inventors

  • BHARDWAJ, JYOTI KIRON
  • Jarausch, Konrad
  • HURLEY, JAY

Assignees

  • RINGO AI, INC.

Dates

Publication Date
20260507
Application Date
20251031
Priority Date
20241031

Claims (20)

  1. CLAIMS
  2. What is claimed is:
  3. 1. A non-transitory medium with instructions stored thereon that, when executed by a processing unit of a computing device that also includes an illuminant and an image sensor, cause the processing unit to perform operations comprising:
  4. acquiring digital images of an object that are generated by the image sensor while the object is differentially illuminated by the illuminant, wherein each of the digital images is generated in conjunction with a different color or intensity of illumination by the illuminant; generating, for the object, a reflectance spectrum based on an analysis of red, green, and blue values of the digital images and a known illumination spectrum that is associated with the illuminant;
  5. establishing a value for a parameter that minimizes a difference between the reflectance spectrum and a reference reflectance spectrum that is associated with the object; and
  6. characterizing a property of the object based on the value established for the parameter.
  7. 2. The non-transitory medium of claim 1, wherein the object is skin, and wherein the parameter is representative of blood volume, blood oxygenation, water content, melanin content, or bilirubin content.
  8. 3. The non-transitory medium of claim 1, wherein the parameter is one of multiple parameters for which values are established to minimize the difference between the reflectance spectrum and the reference reflectance spectrum.
  9. 4. The non-transitory medium of claim 1, wherein said establishing comprises: adjusting the value of the parameter across a range, between a lower threshold and an upper threshold, to create modified reflectance spectrums, each of which is compared against the reference reflectance spectrum to produce a metric that is representative of the difference between that modified reflectance spectrum and the reference reflectance spectrum.
  10. 5. The non-transitory medium of claim 4, wherein said adjusting is performed across the range in its entirety at a fixed interval.
  11. 6. The non-transitory medium of claim 4, wherein said adjusting is performed across a portion of the range, beginning at either the lower threshold or the upper threshold and continuing, at a fixed interval, until the value of the difference begins to increase after a lowest difference is discovered.
  12. 7. The non-transitory medium of claim 1,
  13. wherein the illuminant is a multi-channel illuminant that is able to emit red light, green light, blue light, and white light, and
  14. wherein the image sensor is a Red-Green-Blue (RGB) image sensor that outputs, for each of the digital images, (i) a red spectral response, (ii) a green spectral response, and (iii) a blue spectral response.
  15. 8. The non-transitory medium of claim 7, wherein the operations further comprise:
  16. obtaining, through an analysis of the digital images,
  17. (i) a set of four red spectral responses, which correspond to a first digital image generated in conjunction with emittance of red light by the illuminant, a second digital image generated in conjunction with emittance of green light by the illuminant, a third digital image generated in conjunction with emittance of blue light by the illuminant, and a fourth digital image generated in conjunction with emittance of white light by the illuminant,
  18. (ii) a set of four green spectral responses, which correspond to the first, second, third, and fourth digital images, and
  19. (iii) a set of four blue spectral responses, which correspond to the first, second, third, and fourth digital images. 9. A method performed by a computer program executing on a computing device, the method comprising:
  20. acquiring a first plurality of digital images of an object that are generated by an image sensor with a plurality of sensor channels while the object is differentially illuminated by a light source with a plurality of color channels, wherein each of the first plurality of digital images is generated in conjunction with illumination by a different one of the plurality of color channels;

Description

APPROACHES TO DETERMINING A TARGET MATERIAL VIA IDENTIFICATION AND ANALYSIS OF SPECTRAL INFORMATION USING NON-OCCLUDED ILLUMINATION CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to US Provisional Application No. 63/714,858, titled “Method For Determining A Target Material Via Identification Of Spectra Using Non-Occluded Illumination” and filed on October 31, 2024, and US Provisional Application No. 63/807,065, titled “Method For Determining A Target Material Via Identification Of Spectra Using Non-Occluded Illumination” and filed on May 16, 2025, each of which is hereby incorporated by reference in its entirety. TECHNICAL FIELD [0002] Various embodiments concern computer programs and associated computer-implemented approaches to characterizing a target material via analysis of spectral information. BACKGROUND [0003] Reflectance spectroscopy is a well-established analytical technique for identifying or characterizing a material based on the light it reflects when illuminated by a known source. The technique relies on measuring the spectrum of reflected light and then comparing it to the spectrum of the illumination source to determine wavelengthdependent reflectivity. A known, stable, and sufficiently intense illumination source is essential to overcome system noise - including detector noise, stray light, and electronic interference. Performing measurements in the absence of ambient lighting simplifies this process by eliminating the need to account for uncontrolled or varying external light sources. While several approaches have been proposed or developed for subtracting ambient light contributions, such corrections typically introduce additional noise and uncertainty. [0004] Using these principles, materials can be identified through their specific reflectance signatures within a defined spectral range. The spectral resolution - that is, the ability to distinguish small wavelength differences - is determined by the combined bandwidths of the illumination source, the reflected light collection optics, and the detector. Broadly speaking, three classes of reflectance spectroscopy systems exist: • Systems that employ broadband illumination with narrowband - for example, through scanning or multi-channel - detection; • Systems that use narrowband or tunable illumination with broadband detection; and • Systems that use multi-channel or scanning illumination and detection in combination. [0005] Higher spectral resolution and improved detector sensitivity lead to more accurate material identification, particularly for materials with subtle or overlapping spectral features. BRIEF DESCRIPTION OF THE DRAWINGS [0006] Figure 1A describes a reflectance spectrum for the skin of a living body. [0007] Figure 1 B describes the reflectance spectrums of skin with different material properties. [0008] Figure 2 describes a spectral model for a target configured to reflect different parameter configurations. [0009] Figure 3 illustrates the addition of a parameter into an spectral model for a target. [00010] Figure 4 illustrates the determination of one or more properties of a target by fitting a spectral model developed for the target to a measured curve of a sample of the target. [00011] Figure 5 illustrates a process for differentially illuminating an object for generation of a reflectance spectrum of the object. [00012] Figure 6 illustrates a process for characterizing a material property of a target. [00013] Figure 7 illustrates an example of a computing device with an illuminant and a display for illuminating an object of interest and a number of image sensors for generating digital images in conjunction with illumination events performed by the illuminant or display. [00014] Figure 8 illustrates an example set of spectral outputs of a known illuminant. [00015] Figure 9 illustrates an example set of spectral responses of an image sensor. [00016] Figure 10 includes a schematic diagram of the approach for establishing the spectral fingerprint of an object. [00017] Figure 11 describes a process to determine a target property based on an analysis of values output by a multi-channel image sensor. [00018] Figure 12 illustrates a set of training reflectance spectrums that could be utilized in the process of determining a target property based on an analysis of values output by a multi-channel image sensor. [00019] Figure 13 describes a process to determine a change in a property of an object over time. [00020] Figure 14 describes a process for determining a change in a property, specifically a property of skin, by an application implemented on a computing device. [00021] Figure 15 describes a process for carrying out ABDCE evaluation for skin melanoma detection. [00022] Figure 16 illustrates a network environment that includes a characterization module executing on a computing device. Figure 16 illustrates a network environment 1600 that includes a characterization module 1604 executing