US-20260126767-A1 - PRIMARY ODORS FOR OLFACTORY DIGITIZATION

US20260126767A1US 20260126767 A1US20260126767 A1US 20260126767A1US-20260126767-A1

Abstract

There is presented a technique of providing digital olfactory applications using a set of primary odors. The method comprises: obtaining a set of odors corresponding to inventory of odor items, each odor being characterized by its set of odor-related representative aspects in one or more odor-related spaces; and identifying, within the set of odors, all odors validly covering at least one representative aspect of at least one odor (VCA odors with regard to the at least one odor). Data informative of the VCA odors are processed to identify a set of primary odors for at least one odor. The set of primary odors is used in digital olfactory applications (e.g. reformulating a target odor, maintaining an inventory of odor items, etc.). Processing data informative of the VCA odors to identify a set of primary odors can comprise reducing to a set cover problem and applying a set cover algorithm.

Inventors

Idan Amit
Tzahi KRAWITZ
Assa BENTZUR
Klio MANIATI

Assignees

MOODIFY LTD.

Dates

Publication Date: 20260507
Application Date: 20251230

Claims (20)

1 . A computerized method of providing a digital olfactory application incorporating a digital olfactory technique, the method comprising, by a computer: obtaining data informative of a set of odors corresponding to an inventory of odor items, each odor being characterized by its set of odor-related representative aspects in one or more odor-related spaces; for a first odor, identifying, within the set of odors, all odors that validly cover the set of representative aspect of the first order, thus giving rise to VCA odors with regard to the first odor; processing data informative of the VCA odors to identify a set of primary odors for the first odor; and using the set of primary odors in the digital olfactory application.
2 . The method of claim 1 , wherein at least one representative aspect of the set of representative aspects of the first odor is associated with a binary representative value, and wherein a second odor is considered as validly covering the at least one representative aspect of the first odor when: the second odor belongs to the set of odors; the second odor is characterized by the at least one representative aspect; and the second odor is not characterized by any representative aspect out of the set of representative aspects of the first odor.
3 . The method of claim 1 , wherein at least one representative aspect of the set of representative aspects of the first odor is associated with non-binary representative values, and wherein a second odor is considered as validly covering the at least one representative aspect when: the second odor belongs to the set of odors; the second odor is characterized by the at least one representative aspect with associated representative value that is not lower than respective non-binary representative value of the first odor; and when the second odor is characterized by a representative aspect out of the set of representative aspects of the first odor, a representative value associated with said respective aspect is negligible.
4 . The method of claim 1 , further comprising identifying VCA odors with regard to all odors in the set of odors; and processing data informative of the VCA odors to identify a set of primary odors for all odors in the set of odors.
5 . The method of claim 4 , wherein processing data informative of the VCA odors to identify the set of primary odors for all odors in the set of odors comprises reducing to a set cover problem and applying a set cover algorithm.
6 . The method of claim 1 , wherein identifying the set of primary odors for all odors in the set of odors is provided in accordance with similarity requirements, and wherein the similarity requirements are configured differently for different odor items or groups thereof.
7 . The method of claim 1 , wherein at least part of the odor items is represented by mixtures of respective material sources.
8 . The method of claim 1 , wherein at least part of the primary odors corresponds to mixtures of the odor items from the inventory.
9 . The method of claim 1 , wherein the digital olfactory application is reformulating a target odor, the inventory of odor items comprises odor items testable during the reformulating, the first odor is the target odor and the identified set of primaries is usable for removing from the inventory redundant odor items similarly contributing to the target odor and/or removing irrelevant odor items not contributing into the target odor.
10 . The method of claim 1 , wherein the digital olfactory application is maintaining the inventory of odor items, and the identified set of primaries is usable for optimizing the inventory by removing from an inventory listing at least part of redundant odor items with similar sets of primary odors.
11 . A computing system useable for a digital olfactory application incorporating a digital olfactory technique, the system comprising a processing and memory circuitry (PMC) configured to: obtain data informative of a set of odors corresponding to an inventory of odor items, each odor being characterized by its set of odor-related representative aspects in one or more odor-related spaces; for a first odor, identify, within the set of odors, all odors that validly cover the set of representative aspect of the first order, thus giving rise to VCA odors with regard to the first odor; process data informative of the VCA odors to identify a set of primary odors for the first odor; and use the set of primary odors in the digital olfactory application.
12 . The computing system of claim 11 , wherein a set of odor-related representative aspects is constituted merely by aspects selected, among odor-related aspects characterizing a given odor, in accordance with their importance for similarity between the odors in the set of odors.
13 . The computing system of claim 11 , wherein the PMC is further configured to identify VCA odors with regard to all odors in the set of odors; and process data informative of the VCA odors to identify a set of primary odors for all odors in the set of odors.
14 . The computing system of claim 13 , wherein processing data informative of the VCA odors to identify the set of primary odors for all odors in the set of odors comprises reducing to a set cover problem and applying a set cover algorithm.
15 . The computing system of claim 11 , wherein identifying the set of primary odors for all odors in the set of odors is provided in accordance with similarity requirements, and wherein the similarity requirements are configured differently for different odor items or groups thereof.
16 . The computing system of claim 11 , wherein at least part of the odor items is represented by mixtures of respective material sources.
17 . The computing system of claim 11 , wherein at least part of the primary odors corresponds to mixtures of the odor items from the inventory.
18 . The computing system of claim 11 , wherein the digital olfactory application is reformulating a target odor, the inventory of odor items comprises odor items testable during the reformulating, the first odor is the target odor and the identified set of primaries is usable for removing from the inventory redundant odor items similarly contributing to the target odor and/or removing irrelevant odor items not contributing into the target odor.
19 . The computing system of claim 11 , wherein the digital olfactory application is maintaining the inventory of odor items, and the identified set of primaries is usable for optimizing the inventory by removing from an inventory listing at least part of redundant odor items with similar sets of primary odors.
20 . A non-transitory computer-readable medium comprising instructions that, when executed by a computing system comprising a memory storing a plurality of program components executable by the computing system, cause the computing system to: obtain data informative of a set of odors corresponding to an inventory of odor items, each odor being characterized by its set of odor-related representative aspects in one or more odor-related spaces; for a first odor, identify, within the set of odors, all odors that validly cover the set of representative aspect of the first order, thus giving rise to VCA odors with regard to the first odor; process data informative of the VCA odors to identify a set of primary odors for the first odor; and use the set of primary odors in the digital olfactory application.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of International Application No. PCT/IL2024/050642 filed on Jul. 2, 2024, which claims the priority benefit under 35 U.S.C. § 119 of U.S. Provisional Patent Application No. 63/511,950, filed on Jul. 5, 2023, the contents of each are hereby incorporated in its entirety by reference. TECHNICAL FIELD The presently disclosed subject matter relates to digital olfactory techniques and, more particularly, to digital olfactory techniques involving primary odors. BACKGROUND Digital olfactory is an emerging field that aims to capture, store, recognize and reproduce scents through digital means. Digital olfactory technologies enable generating, transmitting, and receiving smell-enabled digital media usable for communication, gaming, virtual reality, extended reality, e-commerce, automotive and other applications. Likewise, digital olfactory is usable for a wide range of other applications, for example detection of diseases through breath, air quality surveillance, the food and beverage processing, fragrance engineering, etc. An application incorporating a digital olfactory technique is referred to hereinafter as a digital olfactory application. Practical realization of digital olfactory technologies can be achieved with the help of the concept of primary odors. Similarly to primary colors in visual representation, primary odors can be mixed in different proportions to produce a wide spectrum of scents as well as encode and reproduce olfactory information and enable accurate and consistent olfactory experiences across various applications. Identifying a set of primary odors for olfactory digitization is a complex task, and various approaches have been explored, for example: Odor Prism by Hans Henning proposes a prism-shaped graphic representation of six primary odors and their relationships. Burnt, spicy, resinous, foul, fruity, and flowery are the primary odors that occupy the corners of the prism, and each surface represents the positions of odors in accordance with their similarity to the primary odors at the corners of that surface. Chemical Component Analysis proposes identifying the primary odors by analyzing the chemical composition of different odors. Perceptual Space Mapping involves collecting data on how humans perceive similarities and differences between various odors. Multidimensional scaling (MDS) then maps these perceptions into a spatial model, helping to identify primary odors that are perceptually distinct. Principal Component Analysis (PCA) represents each odor by a vector of features and applies a mathematical technique to reduce the dimensionality of odor data while preserving as much variance as possible. Thereby PCA enables discovering clusters of similar odors and identifying the primary odors. GENERAL DESCRIPTION The inventor has appreciated that there is a need to downscale an entire collection of odors items to a set of primary odors from which all other odors in the collection can be derived with a required similarity. The similarity of smells indicates the degree to which it is challenging to distinguish between two or more odors. Odors are considered similar if their discriminability, as assessed by the designated observer(s) (by a human observer and/or by a machine) in the specified odor-related space(s), is below the defined distinction threshold. Direct methods of smell distinction are based on conducting experiments that measure distinction and/or similarity of smells. For example, it is common to measure smell similarity by asking people to rank in a 0 to 100 scale how similar the smells are, asking whether two smells are distinct, asking to find the different smell between three smells, etc. One of the problems with such distinction tests is that they cannot quantify distinct yet close smells. Orange, lemon and gasoline are distinguishable, yet orange and lemon are closer to each other than to gasoline. Indirect methods involve external sources of information for learning smells similarity (e.g. a perceptual database, chemical database, etc.). A common indirect similarity metrics family can be based on the application of general-purpose distance functions (e.g., Euclidean distance, cosine, Jaccard) to smell properties databases. Indirect methods can be based on semantics, chemical, biological properties, and/or perceptual properties. By way of non-limiting example, a review of the smell distinction methods known in contemporary art can be found in the article of Wise et al. (P. M. Wise, M. J. Olsson, and W. S. Cain. Quantification of odor quality. Chemical senses, 25 (4): 429-443, 2000) incorporated herein by reference. Non-limiting example of providing quantified characteristics of similarity is detailed in International Application No. WO24/194866 assigned to the Assignee of the present application and incorporated herewith by reference. Similarity requirements can specify: the observer(s) type (human, mach