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2021 BRN Discussion, page-33491

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StableGenius

116 Posts.

97

31/12/21

20:14

Post #: 58592302

Originally posted by IndepthDiver: ↑

Hi SG,

I'm not discounting Tata but I don't think they will be the first to bring out a fridge with a smell sensor. Those patents you linked are vague, whereas the one for a smell sensing fridge should be more specific.
I'm more inclined to lean towards Samsung.

This first one of their patents I listed below is focused more heavily on detecting the ripeness of food using odors. It talks about preventing food poisoning.

The second one detects ripeness through camera images (which reminds you about the video Brainchip showed some time ago with the ripe and unripe bananas and apples etc). This one talks about using an AI processor and the technology simulates functions of the human brain. It talks about using an odor sensor and combining that with the AI system as part of one of the claims. This one is the newest of the two and the most likely one IMO.

Pure speculation, DYOR

Patent 1: Using an odor sensor

https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=1&f=G&l=50&d=PG01&p=1&S1=20210055275&OS=20210055275&RS=20210055275
United States Patent Application 20210055275
1 Kind Code A1
2 PARK; Jangpyo ; et al. February 25, 2021
ELECTRONIC DEVICE AND METHOD FOR DETERMINING KIND AND STATE OF FOOD STORED THEREIN

Abstract
The disclosure relates to an electronic device and a method for determining the kind and the state of a food stored therein. The electronic device includes a storage container in which the food is stored, a gas detecting device for detecting a gas included in air in the storage container, and at least one processor that identifies a time point, at which the gases included in the air in the storage container are extracted, on the basis of at least one temperature profile of the gas detecting device, and identifies a time point at which the gases included in the air in the storage container are extracted, on the basis of a configuration of at least one adsorption material included in the gas detecting device.
Inventors: PARK; Jangpyo;(Suwon-si, KR); JEONG; Yongwon;(Suwon-si, KR); KIM; Seonghwan;(Suwon-si, KR); PARK; Heejin;(Suwon-si, KR); KIM; Joonho;(Suwon-si, KR)
Applicant:
Name City State Country Type

SAMSUNGELECTRONICS CO., LTD.
Suwon-si

KR
Assignee: SAMSUNGELECTRONICS CO., LTD
Suwon-si
KR
Family ID: 74645295
Appl. No.: 17/001138
Filed: August 24, 2020

Current U.S. Class: 1/1
Current CPC Class: G01N 1/2214 20130101; F25D 2700/06 20130101; G01N 33/02 20130101; G01N 33/0011 20130101; G01N 21/783 20130101; G01N 33/0062 20130101; G01N 1/2226 20130101; F25D 17/042 20130101
International Class: G01N 33/02 20060101 G01N033/02; G01N 21/78 20060101 G01N021/78; G01N 33/00 20060101 G01N033/00

Foreign Application Data
Date Code Application Number
Aug 22, 2019 KR 10-2019-0103299
Claims

1. An electronic device usable for storing foods, the electronic device comprising: a storage container; an adsorption member configured to adsorb at least a portion of gas contained in air in the storage container; a heater configured to heat the adsorption member; a gas detecting device comprising a sensor configured to sense gas desorbed based on the adsorption member being heated; and at least one processor configured to detect at least one of a kind and a concentration of the desorbed gas generated from food stored in the storage container, the at least one processor configured to: control the heater to heat the adsorption member; receive, from the gas detecting device, information on the desorbed gas based on the adsorption member being heated; identify a time point at which the desorbed gas is extracted from the adsorption member based on the received information on the desorbed gas; detect the at least one of the kind and the concentration of the desorbed gas based on at least one of a temperature change of the adsorption member according to the heating of the adsorption member, a predetermined configuration of at least one adsorption material contained in the adsorption member, and the time point at which the desorbed gas is extracted from the adsorption member; and determine a kind and a state of the food based on the detected at least one of the kind and the concentration of the desorbed gas.

2. The electronic device of claim 1, wherein the state of the food comprises at least one of the state of the food according to a lapsed time after the food is stored, the state of the food according to a freshness or a ripening degree of the food, a cooked state of the food, and a frozen state of the food.

3. The electronic device of claim 1, wherein the gas detecting device is inside the storage container, and the storage container is a dedicated container for determining the kind and the state of the food.

4. The electronic device of claim 1, wherein the gas detecting device further comprises: a housing; an opening/closing unit disposed on at least one side of the housing; and a sensor configured to sense the gas desorbed from the adsorption member, and wherein the adsorption member is accommodated in an interior of the housing, and adsorbs the gas contained in the air when the opening/closing unit is opened.

5. The electronic device of claim 4, wherein the housing of the gas detecting device forms an at least temporarily closed space according to an operation of the opening/closing unit.

6. The electronic device of claim 4, wherein the opening/closing unit is configured to allow the air in the storage container to flow into the interior of the housing through diffusion.

7. The electronic device of claim 4, wherein the gas detecting device further comprises a scrubber configured to collect the gas generated from the food.

8. The electronic device of claim 4, further comprising: a filter configured to extract a target gas by filtering material desorbed from the adsorption member.

9. The electronic device of claim 8, wherein the filter comprises a porous material comprising a metal organic framework (MOF).

10. The electronic device of claim 9, wherein a metallic material in the MOF comprises at least one of Pt, Zn Cu, Be, Fe, Ni, W, Co, Mn, Mo, Cr, Mg, V, Li, Ca, and Na.

11. The electronic device of claim 9, wherein the MOF comprises a material having at least one functional group of --COOCu, --COOAg, --HSO4, --COOLi, --SO3H, --OP(.dbd.O)OH2, --P(.dbd.O)(OH)2, --OH, and --COOH.

12. The electronic device of claim 1, wherein the at least one processor is further configured to control a temperature of the storage container according to the determined kind and the determined state of the food.

13. A method for determining at least one of a kind and a state of foods in an electronic device, the method comprising: sampling, using a gas detecting device, gas contained in air in a storage container configured to store food; identifying an extraction time point of the sampled gas while extracting the sampled gas toward the outside of the gas detecting device; and detecting at least one of a kind and a concentration of the gas based on at least one of a temperature change of the gas detecting device, a configuration of a gas marking material contained in the gas detecting device, and the extraction time point of the sampled gas.

14. The method of claim 13, wherein the sampling comprises: adsorbing at least a portion of the gas contained in the air in the storage container to an adsorption member.

15. The method of claim 14, wherein the detecting comprises: heating the adsorption member; receiving information of gas desorbed from the adsorption member, from the gas detecting device; identifying a time point at which the desorbed gas is extracted based on the received information; and detecting the at least one of the kind and the concentration of the desorbed gas based on at least one of a temperature change of the adsorption member according to the heating of the adsorption member, a predetermined configuration of at least one adsorption material contained in the adsorption member, and the time point at which the desorbed gas is extracted from the adsorption member.

16. The method of claim 15, wherein the detecting further comprises: determining the kind and the state of the food by comparing the at least one of the kind and the concentration of the gas detected by the gas detecting device and information contained in a database stored in the electronic device or a server outside the electronic device.

17. The method of claim 13, wherein the sampling and the detecting are performed according to a command of a user or a preset period.

18. The method of claim 13, further comprising determining the kind and the state of the food according to the detecting and controlling a temperature or a humidity of the storage container according to the determined kind and the determined state of the food.

19. The method of claim 13, further comprising determining the kind and the state of the food according to the detecting, wherein the state of the food comprises at least one of the state of the food according to a lapsed time after the food is stored, the state of the food according to a freshness or a ripening degree of the food, a cooked state of the food, and a frozen state of the food.

20. An electronic device comprising: a memory storing instructions; and at least one processor configured to execute the instructions to: control a heater to heat an adsorption member for adsorbing at least a portion of gas contained in air in a storage container; receive information on gas desorbed based on the adsorption member being heated; identify a time point at which the desorbed gas is extracted from the adsorption member based on the received information on the desorbed gas; detect at least one of a kind and a concentration of the desorbed gas based on at least one of a temperature change of the adsorption member according to the heating of the adsorption member, a predetermined configuration of at least one adsorption material contained in the adsorption member, and the time point at which the desorbed gas is extracted from the adsorption member; and determine a kind and a state of food stored in the storage container based on the detected at least one of the kind and the concentration of the desorbed gas.Description

CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application is based on and claims priority under 35 U.S.C. .sctn. 119 to Korean Patent Application No. 10-2019-0103299, filed on Aug. 22, 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety.

BACKGROUND

1. Field

[0002] The disclosure relates to an electronic device and a method for determining the kind and the state of a food stored therein.

2. Description of Related Art

[0003] A general refrigeration system is a device that cools the interiors of a refrigeration chamber and a freezing chamber formed in the interior thereof and freshly stores foods in the refrigeration chamber and the freezing chamber for a long time.

[0004] The freshness of the foods stored in the refrigerator may be maintained for a certain period of time, but the foods ripen or eventually decompose over time. A user may monitor the foods by using a memory of the initial storage time point of the foods or by the naked eye, or may determine the ripening degree and the decomposition degree of the foods stored in the refrigerator through recognition, for example, of a scent. However, by these methods, the precision of monitoring the ripening degrees and the decomposition of the foods is low, and the user may forget and frequently miss the expiration date within which the foods can be ingested freshly. Further, the user may be exposed to diseases or illness, such as food poisoning, by ingesting the decomposed foods. Accordingly, a measure for securing safe intake of foods by predicting the ripening and decomposition states of foods has been studied. For example, a measure of predicting the kind and the state of a food by detecting the gases generated from the food has been studied. If a food is decomposed, various kinds of gases, for example, ammonia, hydrogen sulfide, methane, and the like may be generated.

SUMMARY

[0005] Provided is an electronic device and a method for detecting a kind and a state of food by measuring gases generated by the food in a storage chamber of the electronic device.

[0006] Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the presented embodiments.

[0007] In accordance with an aspect of the disclosure, an electronic device usable for storing foods includes: a storage container; an adsorption member configured to adsorb at least a portion of gas contained in air in the storage container; a heater configured to heat the adsorption member; a gas detecting device including a sensor configured to sense gas desorbed based on the adsorption member being heated; and at least one processor configured to detect at least one of a kind and a concentration of the desorbed gas generated from food stored in the storage container, the at least one processor configured to: control the heater to heat the adsorption member; receive, from the gas detecting device, information on the desorbed gas based on the adsorption member being heated; identify a time point at which the desorbed gas is extracted from the adsorption member based on the received information on the desorbed gas; detect the at least one of the kind and the concentration of the desorbed gas based on at least one of a temperature change of the adsorption member according to the heating of the adsorption member, a predetermined configuration of at least one adsorption material contained in the adsorption member, and the time point at which the desorbed gas is extracted from the adsorption member; and determine a kind and a state of the food based on the detected at least one of the kind and the concentration of the desorbed gas.

[0008] The state of the food may include at least one of the state of the food according to a lapsed time after the food is stored, the state of the food according to a freshness or a ripening degree of the food, a cooked state of the food, and a frozen state of the food.

[0009] The gas detecting device may be inside the storage container, and the storage container may be a dedicated container for determining the kind and the state of the food.

[0010] The gas detecting device may further include: a housing; an opening/closing unit disposed on at least one side of the housing; and a sensor configured to sense the gas desorbed from the adsorption member, and wherein the adsorption member is accommodated in an interior of the housing, and adsorbs the gas contained in the air when the opening/closing unit is opened.

[0011] The housing of the gas detecting device may form an at least temporarily closed space according to an operation of the opening/closing unit.

[0012] The opening/closing unit may be configured to allow the air in the storage container to flow into the interior of the housing through diffusion.

Patent 2: Using a camera in the refrigerator
https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=1&f=G&l=50&d=PG01&p=1&S1=20200097776&OS=20200097776&RS=20200097776
United States Patent Application 20200097776
Kind Code A1
KIM; Jaehwan ; et al. March 26, 2020
METHOD AND SYSTEM FOR PROVIDING INFORMATION RELATED TO A STATUS OF AN OBJECT IN A REFRIGERATOR

Abstract
A method of providing information related to a state of an object in a refrigerator includes obtaining a first camera image including at least one object kept in the refrigerator through a camera arranged in the refrigerator, obtaining environmental information in the refrigerator through an environmental sensor arranged in the refrigerator,

predicting information related to a current state of the at least one object by applying the first camera image including the at least one object and the environmental information in the refrigerator to an artificial intelligence (AI) model

; and providing the information related to the current state of the at least one object.
Inventors: KIM; Jaehwan;(Suwon-si, KR); Baiev; Oleksandr;(Suwon-si, KR)
Applicant:
Name City State Country Type

SAMSUNG ELECTRONICS CO., LTD.
Suwon-si

KR
Assignee: SAMSUNG ELECTRONICS CO., LTD.
Suwon-si
KR
Family ID: 69883237
Appl. No.: 16/572075
Filed: September 16, 2019

Current U.S. Class: 1/1
Current CPC Class: G06K 9/6267 20130101; G06N 3/084 20130101; H04L 67/12 20130101; G06K 9/00771 20130101; G01N 33/0034 20130101; G06T 7/0002 20130101; H04N 7/183 20130101; G06K 9/6288 20130101; F25D 29/00 20130101; G06K 9/4628 20130101; G06T 7/97 20170101; F25D 2700/06 20130101; G06K 2209/17 20130101; H04N 5/232 20130101; H04N 5/23299 20180801; G01N 33/02 20130101; F25D 2500/06 20130101; G01N 33/025 20130101; G06N 3/0445 20130101; G06T 7/70 20170101
International Class: G06K 9/62 20060101 G06K009/62; G06T 7/00 20060101 G06T007/00; G06T 7/70 20060101 G06T007/70; H04N 5/232 20060101 H04N005/232; G01N 33/02 20060101 G01N033/02

Foreign Application Data
Date Code Application Number
Sep 21, 2018 KR 10-2018-0114377
Claims

1. A method of providing information related to a state of an object in a refrigerator, the method comprising: obtaining a first camera image comprising at least one object kept in the refrigerator through a camera arranged in the refrigerator; obtaining environmental information in the refrigerator through an environmental sensor arranged in the refrigerator; predicting information related to a current state of the at least one object by applying the first camera image comprising the at least one object and the environmental information in the refrigerator to an artificial intelligence (AI) model; and providing the information related to the current state of the at least one object.

2. The method of claim 1, wherein the providing of the information related to the current state of the at least one object comprises: providing information about a consumable period of the at least one object.

3. The method of claim 1, wherein the providing of the information related to the current state of the at least one object comprises: providing information about a ripeness or a freshness of the at least one object.

4. The method of claim 1, wherein the environmental sensor comprises at least one of a temperature sensor, a humidity sensor, or an odor sensor, and the obtaining of the environmental information in the refrigerator comprises obtaining at least one of temperature information, humidity information, or odor information in the refrigerator.

5. The method of claim 1, wherein the obtaining of the environmental information in the refrigerator comprises obtaining temperature information of a temperature around the at least one object by using a temperature sensor, and the predicting of the information related to the current state of the at least one object comprises predicting the information related to the current state of the at least one object by applying the first camera image and the temperature information to the AI model.

6. The method of claim 1, wherein the obtaining of the environmental information in the refrigerator comprises obtaining odor information of the at least one object by using an odor sensor arranged to be adjacent to the at least one object, and the predicting of the information related to the current state of the at least one object comprises predicting the information related to the current state of the at least one object by applying the first camera image and the odor information to the AI model.

7. The method of claim 1, wherein the predicting of the information related to the current state of the at least one object comprises: obtaining a spectrometric image with respect to the at least one object by using a spectrometric sensor arranged in the refrigerator; and predicting the information related to the current state of the at least one object by applying the first camera image, the spectrometric image, and the environmental information in the refrigerator to the AI model.

8. The method of claim 1, wherein the predicting of the information related to the current state of the at least one object comprises: determining whether a gas detection quantity measured by at least one odor sensor from among a plurality of odor sensors arranged in the refrigerator exceeds a critical value; when the gas detection quantity exceeds the critical value, generating an odor distribution map based on odor information obtained from the plurality of odor sensors; and comparing the first camera image comprising the at least one object with the odor distribution map and identifying a first object of the at least one object including a spoilage probability that is greater than a reference value.9. The method of claim 8, wherein the identifying of the first object comprises: obtaining spectrometric information about the first object by using a spectrometric sensor arranged in the refrigerator; and determining a degree of spoilage of the first object by using the spectrometric information.

10. The method of claim 8, further comprising: when the first object is totally or partially hidden by a second object of the at least one object in the first camera image, providing notification information about a location where the first object is kept in the refrigerator.

11. The method of claim 8, further comprising: controlling a position of the camera to capture the identified first object.

12. The method of claim 1, wherein the obtaining of the first camera image comprises obtaining the first camera image comprising the at least one object at a first time point through the camera, the method further comprising: obtaining a second camera image comprising the at least one object at a second time point, which is after the first time point, through the camera; obtaining information related to a state of the at least one object at the second time point and a degree of a state change of the at least one object between the first time point and the second time point, by applying the first camera image, the second camera image, and environmental information at the second time point to the AI model; and controlling an image detection interval of the camera, based on at least one of the information related to the state of the at least one object at the second time point or the degree of the state change of the at least one object.

13. A refrigerator comprising: a camera configured to obtain a first camera image comprising at least one object kept in the refrigerator; an environmental sensor configured to obtain environmental information in the refrigerator; and a processor configured to predict information related to a current state of the at least one object by applying the first camera image and the environmental information in the refrigerator to an artificial intelligence (AI) model; and an outputter configured to provide the information related to the current state of the at least one object.

14. The refrigerator of claim 13, wherein the information related to the current state of the at least one object comprises at least one of information about a consumable period of the at least one object, information about a ripeness of the at least one object, or information about a freshness of the at least one object.

15. The refrigerator of claim 13, wherein the environmental sensor comprises a temperature sensor, and the processor is further configured to: obtain temperature information of a temperature around the at least one object by using the temperature sensor, and predict the information related to the current state of the at least one object by applying the first camera image and the temperature information to the AI model.

16. The refrigerator of claim 13, wherein the environmental sensor comprises an odor sensor arranged to be adjacent to the at least one object, and the processor is further configured to: obtain odor information of the at least one object by using the odor sensor, and predict the information related to the current state of the at least one object by applying the first camera image comprising the at least one object and the odor information of the at least one object to the AI model.

17. The refrigerator of claim 16, wherein the processor is further configured to: obtain a spectrometric image about the at least one object by using a spectrometric sensor arranged in the refrigerator, and predict the information related to the current state of the at least one object by applying the first camera image, the spectrometric image, and the environmental information in the refrigerator to the AI model.

18. The refrigerator of claim 13, wherein the processor is further configured to: determine whether a gas detection quantity measured by at least one odor sensor from among a plurality of odor sensors arranged in the refrigerator exceeds a critical value, generate an odor distribution map based on odor information obtained from the plurality of odor sensors, when the gas detection quantity exceeds the critical value, and compare the first camera image comprising the at least one object with the odor distribution map and identify a first object of the at least one object including a spoilage probability that is greater than a reference value.

19. The refrigerator of claim 18, wherein the processor is further configured to: obtain spectrometric information about the first object by using a spectrometric sensor arranged in the refrigerator, and determine a degree of spoilage of the first object by using the spectrometric information.

20. A computer program product comprising a recording medium having recorded thereon a program to execute: an operation of obtaining a first camera image comprising at least one object kept in a refrigerator through a camera arranged in the refrigerator, an operation of obtaining environmental information in the refrigerator through an environmental sensor arranged in the refrigerator, an operation of predicting information related to a current state of the at least one object by applying the first camera image comprising the at least one object and the environmental information in the refrigerator to an artificial intelligence (AI) model, and an operation of providing the information related to the current state of the at least one object.Description

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is based on and claims priority under 35 U.S.C. .sctn. 119 to Korean Patent Application No. 10-2018-0114377, filed on Sep. 21, 2018, in the Korean Intellectual property Office, the disclosure of which is incorporated by reference herein in its entirety.

BACKGROUND

1. Field

[0002] The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm, such as deep learning, etc., and an application of the AI system, and more particularly, to a method and a system for providing information related to a state of an object stored in a refrigerator by using the AI system.

2. Description of Related Art

[0003] An artificial intelligence (AI) system is a computer system with human level intelligence, and unlike a previous rule-based smart system, the AI system is a system that trains itself, decides, and becomes increasingly smarter. The more the AI system is used, the higher the recognition rate of the AI system and the AI system may more accurately understand a user's taste. Thus, the previous rule-based smart system has been gradually replaced by a deep learning-based AI system.

[0004] AI technologies are composed of machine learning (for example, deep learning) and element technologies using the machine learning.

[0005] Machine learning is an algorithm technology that classifies/learns characteristics of input data on its own. The element technology is a technology that simulates functions of the human brain, such as recognition, determination, etc., by using machine learning algorithms, such as deep learning, etc., and includes technical fields of linguistic understanding, visual comprehension, inference/prediction, knowledge representation, operation control, etc.

[0006] AI technology is applied to various fields as follows. The linguistic understanding is a technology to recognize and apply/process human language/characters and includes processing of natural language, machine translation, a conversation system, query response, speech recognition/synthesis, etc. The visual comprehension is a technology to recognize and process objects as in human vision and includes object recognition, object detection, image search, human recognition, scene understanding, spatial understanding, image enhancement, etc. The inference prediction is a technology to examine and logically infer and predict information and includes inference based on knowledge/probabilities, optimization prediction, preference-based planning, recommendation, etc. The knowledge representation is a technology to automatically process human experience data into knowledge data and includes knowledge establishment (data generation/classification), knowledge management (data usage), etc. The operation control is a technology to control autonomous driving of a vehicle and motions of a robot and includes motion control (navigation, collision avoidance, driving), manipulation control (behavior control), etc.
Expand
HiIDD,
You’re another whose opinion I value so more than happy to be corrected by someone who understands this industry much better than me. Thanks.
I agree,Tata’s patent is very general. I just came across it and thought it was interesting so I would share it.
I started going through theSamsungNNpatents and they have hundredsof them.It would be massive if Samsungwere to useAKIDAbecause they are such a big playerwith so many products we could be put init woulddefinitelyhelp us become ubiquitous!
A different topic: surprisedBrainchipis not attending the CES as it’s billed as the most influential tech show around. I’ve been looking at that and there are so many huge companies that are going to be there. Although it does look more like it shows end products or ready to go platforms for plug and play to go straight into devices/cars.
I didn’t know whoVinfastwas so I had a look at them. They are building cars and have chosen theCerencefor their systems. I mentionedCerencea few days ago and would love to be in their systems as well as they are massive.
I don’t understand how all these companies have patentsbut not a workingNNsystem or chip. I am hopeful that means they will be relying onBRAINCHIPto supply the actualNN!
HNY
Hi IDD,
You’re another whose opinion I value so more than happy to be corrected by someone who understands this industry much better than me. Thanks.
I agree, Tata’s patent is very general. I just came across it and thought it was interesting so I would share it.
I started going through the Samsung NN patents and they have hundreds of them. It would be massive if Samsung were to use AKIDA because they are such a big player with so many products we could be put in it would definitely help us become ubiquitous!
A different topic: surprised Brainchip is not attending the CES as it’s billed as the most influential tech show around. I’ve been looking at that and there are so many huge companies that are going to be there. Although it does look more like it shows end products or ready to go platforms for plug and play to go straight into devices/cars. Hopefully we will get a surprise mention in a system or 2.
I didn’t know who Vinfast was so I had a look at them. They are building cars and have chosen the Cerence for their systems. I mentioned Cerence a few days ago and would love to be in their systems as well as they are massive.
I don’t understand how all these companies have patents but not a working NN system or chip. I am hopeful that means they will be right elying on BRAINCHIP to supply the actual NN!
Looking forward to the “Run like a deer: Autonomously” presentationon Jan 4.
HNY
Hi IDD, You’re another whose opinion I value so more than happy to be corrected by someone who understands this industry much better than me. Thanks.I agree, Tata’s patent is very general. I just came across it and thought it was interesting so I would share it. I started going through the Samsung NN patents and they have hundreds of them. It would be massive if Samsung were to use AKIDA because they are such a big player with so many products we could be put in it would definitely help us become ubiquitous! A different topic: surprised Brainchip is not attending the CES as it’s billed as the most influential tech show around. I’ve been looking at that and there are so many huge companies that are going to be there. Although it does look more like it shows end products or ready to go platforms for plug and play to go straight into devices/cars. Hopefully we will get a surprise mention in a system or 2. I didn’t know who Vinfast was so I had a look at them. They are building cars and have chosen the Cerence for their systems. I mentioned Cerence a few days ago and would love to be in their systems as well as they are massive. I don’t understand how all these companies have patents but not a working NN system or chip. I am hopeful that means they will be relying on BRAINCHIP to supply the actual NN! Looking forward to the “Run like a deer: Autonomously” presentation on Jan 4. HNYHi IDD,
You’re another whose opinion I value so more than happy to be corrected by someone who understands this industry much better than me. Thanks.
I agree, Tata’s patent is very general. I just came across it and thought it was interesting so I would share it.
I started going through the Samsung NN patents and they have hundreds of them. It would be massive if Samsung were to use AKIDA because they are such a big player with so many products we could be put in it would definitely help us become ubiquitous!
A different topic: surprised Brainchip is not attending the CES as it’s billed as the most influential tech show around. I’ve been looking at that and there are so many huge companies that are going to be there. Although it does look more like it shows end products or ready to go platforms for plug and play to go straight into devices/cars. Hopefully we will get a surprise mention in a system or 2.
I didn’t know who Vinfast was so I had a look at them. They are building cars and have chosen the Cerence for their systems. I mentioned Cerence a few days ago and would love to be in their systems as well as they are massive.
I don’t understand how all these companies have patents but not a working NN system or chip. I am hopeful that means they will be relying on BRAINCHIP to supply the actual NN!
Looking forward to the “Run like a deer: Autonomously” presentationon Jan 4.
HNY
Hi IDD,
You’re another whose opinion I value so more than happy to be corrected by someone who understands this industry much better than me. Thanks.
I agree, Tata’s patent is very general. I just came across it and thought it was interesting so I would share it.
I started going through the Samsung NN patents and they have hundreds of them. It would be massive if Samsung were to use AKIDA because they are such a big player with so many products we could be put in it would definitely help us become ubiquitous!
A different topic: surprised Brainchip is not attending the CES as it’s billed as the most influential tech show around. I’ve been looking at that and there are so many huge companies that are going to be there. Although it does look more like it shows end products or ready to go platforms for plug and play to go straight into devices/cars. Hopefully we will get a surprise mention in a system or 2.
I didn’t know who Vinfast was so I had a look at them. They are building cars and have chosen the Cerence for their systems. I mentioned Cerence a few days ago and would love to be in their systems as well as they are massive.
I don’t understand how all these companies have patents but not a working NN system or chip. I am hopeful that means they will be relying on BRAINCHIP to supply the actual NN!
Looking forward to the “Run like a deer: Autonomously” presentationon Jan 4.
HNY

BRN Price at posting: 68.0¢ Sentiment: Buy Disclosure: Held

0 Great analysis

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(20min delay)
Last 20.0¢		Change 0.000(0.00%)		Mkt cap ! $380.4M

Open	High	Low	Value	Volume
19.5¢	20.5¢	19.5¢	$656.1K	3.281M

Buyers (Bids)

No.	Vol.	Price($)
16	364081	20.0¢

Sellers (Offers)

Price($)	Vol.	No.
20.5¢	1348618	18

View Market Depth

No.	Vol.	Price($)
16	364081	0.200
44	1194171	0.195
63	2165365	0.190
32	1383291	0.185
49	1699888	0.180

Price($)	Vol.	No.
0.205	1348618	18
0.210	1248206	20
0.215	432560	19
0.220	724111	24
0.225	361409	12

Last trade - 16.10pm 12/07/2024 (20 minute delay)

BRN (ASX) Chart