Description:FIELD OF INVENTION:
This invention in general relates to label-based identification of hand-made artifacts, and more particularly relates to gesture-based chronicling of handmade artifacts.
BACKGROUND OF THE INVENTION
In the 20th century when there was scarcity of machinery most of the products, artifacts were handmade. As we moved on to the 21st century, evolu-tion of machinery was in evident and replaced most of the handmade process of making an artifact with intelligent machinery. The machinery manufactures the artifacts more precisely than the humans also, the speed of manufacturing the artifacts is much more than that of the humans.
However, with the increased machinery-based artifacts it is very diffi-cult to difference between a handmade product or a machinery-based product. In this machine age there still persists a demand for handmade artifacts. Fur-ther, it is also difficult to distinguish between the handmade products that are specific to geographical regions. Sometimes products are manufactured through machine and are labelled as handmade products. Efforts to trace the origin of the handmade of artifacts have been made in the prior art that are discussed as under.
CN210955161U to TOREAD HOLDINGS GROUP CO LTD relates to a smart label capable of autonomously controlling and sending information to secure the stored information. The invention provides a wearable device also adopting a smart tag to improve the safety of the wearable device. The weara-ble device are objects such as clothing, scarf, backpack or bracelet. The inven-tion also discloses the use of cloth as a wearable device has better anti-counterfeiting performance.
CN106228379A to SHANGHAI LINGUO IND CO. LTD. relates to an anti-counterfeiting device. The device includes an authentic information generating module and label. The tag as disclosed in the invention is configured to store dynamic authentication information and is a near field communication (NFC) tag or a radio frequency identification (RFID) tag. The user terminal of the anti-counterfeiting device of the disclosed invention is a smartphone or a tablet or a wearable device or a personal digital assistant.
WO2021091604A1 to APPLE INC. relates to a system and device for machine-learning based gesture recognition. The device implements a system for machine learning based gesture recognition. The invention discloses electronic devices and in particular detecting gestures made by a user wearing or otherwise operating an electronic device. The device also discloses a binary label for sensor data that indicate a gesture of human wearing a wearable device such as a smart-watch or band or the like.
There is a need to identify the origin of the artifacts through label based scanning system. There is also a need for logging the details of artisans through gesture.
SUMMARY OF THE INVENTION:
A system for identification of hand-made artifacts using gesture recog-nition by artisans includes an artifact crafted by the artisans including a unique label for identifying authenticity of the artifact. A wearable device worn by the artisans while crafting an artifact, the wearable device including plurality of sensors for capturing gestures from the artisan. A processing unit for processing the data received through plurality of sensors, the processing unit includes a database for storing raw data. An analytics module for processing the raw data, a machine learning module for processing the raw data. An artisan profile for storing artisan data and the label configured by deriving data from an artisan profile. A handheld device scanning the label for authenticating the artifact in the system.
The artisan profile including a primary signature configured to including artisan data. The artisan profile including a secondary signature configured to encode gestures of the artisan. The artisan profile including a third signature configured to including artisan details. The artisan profile including a fourth signature configured to include artifact data. The machine learning module arti-ficial intelligence, deep learning algorithms for extracting useful data. The ana-lytics module analyses useful data and creates the artisan profile. The includes an origin validation module configured to trace the origin of the artisan.
A method for identification of hand-made artifacts using gesture recog-nition of the system including wearing the wearable device; receiving, artisan data from the primary signature; recording, gestures of artisan through plurality of sensors; encoding, set of gestures of the artisan from the secondary signa-ture; receiving, artisan data from the third signature; creating, artisan profile by processing first, second and third signatures on the processor; processing the raw data using machine learning module; extracting useful data from raw data and creating artisan profile; embedding label data into the label; and embed-ding label in the artifact. The consumer accessing the label data by scanning label of the artifact through the handheld device.

BRIEF DESCRIPTION OF DRAWINGS:
The objectives and advantages of the present invention will become apparent from the following description read in accordance with the accompanying drawings wherein
FIG. 1 shows a system for identification of hand-made artifacts using gesture recognition in accordance with the present invention;
FIG. 2 shows a process for creating artisan profile of the system for identifica-tion of hand-made artifacts of FIG. 1;
FIG. 3 shows a system architecture of the system for identification of hand-made artifacts using gesture recognition of FIG. 1;
FIG. 4 shows a flow chart of the system for identification of hand-made arti-facts using gesture recognition of FIG. 1;
FIG. 4A is a continued flow chart of the system for identification of hand-made artifacts using gesture recognition of FIG. 4;
FIG. 5 shows data representation obtained from Hand Spinning activity using the system for identification of hand-made artifacts using gesture recognition; and
FIG. 5A shows data representation obtained from Hand Embroidery activity using the system for identification of hand-made artifacts using gesture recog-nition.

DETAILED DESCRIPTION OF THE INVENTION
The invention described herein is explained using specific exemplary de-tails for better understanding. However, the invention disclosed can be worked on by a person skilled in the art without the use of these specific details.
References in the specification to "one embodiment" or "an embodi-ment" means that particular feature, structure, characteristic, or function de-scribed in connection with the embodiment is included in at least one embodi-ment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
References in the specification to “preferred embodiment” means that a particular feature, structure, characteristic, or function described in detail here-by omitting known constructions and functions for clear description of the pre-sent invention.
The foregoing description of specific embodiments of the present inven-tion has been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching.
Referring to FIG. 1, a system for identification of hand-made artifacts using gesture recognition 100 (hereinafter referred to as “system 100”) is de-scribed. Accordingly, the system 100 includes a plurality of wearable devices 125, a processing unit 135 and a label that is integrable with the artifact 140. The system 100 assists customers to validate if an artifact 140 under considera-tion is handmade. The system 100 also helps the customer to get the details about an artisan 105 and the artifact 140. It is understood that an artisan 105 is a person that have acquired skills for crafting one or more artifacts 140.
In accordance with the present invention, the wearable devices 125 are configured according to the present invention that are worn by the artisan 105 while crafting the artifacts 140. The processing unit 135 processes data on a cloud processing environment. The label is preferably embedded in the artifact 140. The artisans 105 information, data of wearable devices etc. is processed on the processing unit 135. In this embodiment the preferred wearable devices are wrist band, smart rings, wire gloves, smart spectacles, and the like that are configured in accordance with the present invention. However, the wearable devices vary in other embodiments of the present invention.
The system 100 is also accessible to an Intermediaries 110 that prefera-bly are wholesalers or dealers that make the artifacts 140 available to plurality of retailers 115. The retailers 115 make the artifacts 140, made in accordance with the present invention, available to the consumers 120 to purchase the arti-facts 140. The consumers 120 uses a plurality of handheld devices 130 to trace the origin of the artifacts 140 preferably by scanning the label made according to the present invention i.e. attached to the artifact.
Referring to FIG. 2, a process for creating artisan profile with the sys-tem 100 is described. It is noted that the artisans 105 who create various artistic work move their body parts in some typical patterns these movement includes movements of fingers, hand, gestures, feet etc. hereinafter referred as set of gestures 205. It is understood, however, that for crafting different artifacts 140 an artisan 105 shows different gestures 205. These set of gesture 205 are dif-ferent for different artifacts 140 and varies from one artisan 105 to another. In an exemplary embodiment, the gestures for painting, hand printing, casting, hand embroidery, jewelry, stone, carving, wood and the like differs from arti-san to artisan.
In accordance with the present invention, the gestures 205 of an artisan 105 are advantageously captured using the wearable devices 125. The wearable devices 125 are worn by the artisans 105 while crafting the artifacts 140. The type of wearable devices 125 varies for crafting different artifacts 140.
In the process of creating the profile of the artisans 105, in a first step, the artisan 105 is instructed to wear the wearable device 125 configured ac-cording to the present invention. In this step, a primary signature is configured by entering artisan data in the processor. The primary signature includes data such as Artisan Name, Gender, ID type, ID Number, Age, Cluster, Weaving type, Loom type, Products produced, Product ID etc. that are provided to a processor 325 of the wearable 125 device in the form of inputs.
In a second step, the identity of the artisan 105 wearing the wearable device 125 is validated preferably by a touch input or pattern or the like. In this step, if the identity of the artisan 105 is validated then the control goes to a next step.
In a third step, the wearable device 125 initiates a process of capturing gestures 205 of the artisan 105 through pluralities of sensors 315. In this step, the processor 325 of the wearable device 125 encodes the gestures 205 to cre-ate a secondary signature from the gestures that are recorded in the previous step.
In a fourth step, a third signature is created by the processor 135. The third signature includes details such as location, timestamp etc. In a next step, the artisan profile 240 is created by combining the primary signature, the sec-ondary signature and the third signature.
In accordance with the present invention, the wearable device 125 transmitting the artisan profile 240 including the artisan signature and artisan data. captured through the wearable devices 125 towards the processing unit 135 by handheld device 130.The processing unit 135 remotely receives the data received from the plurality of wearable devices 125 and stores plurality of ar-tisan profile 240. The artisan profile 240 is processed on the processing unit135. In an exemplary embodiment, the artisan 105 wears a smart band on the wrist while painting to record the gestures 205. In another exemplary embodiment, the artisan 105 wears wired gloves for crafting intricate embroideries while crafting artifacts 140.
For example, in this preferred embodiment the primary signature of the artisan 105 stored in the artisan profile 240 includes: Artisan name: Ma-heshwari, Gender: Female, ID type: Artisan card, ID Number: ABC12348910, Age: 40, Cluster: Budwan, Weaving type: Jamdani, Loom type: Pitloom, Prod-ucts produced, Product ID: XYZABC123, Start date /time : 12/12/21 10hr: 12min:25Sec, Scan date/time: 12/12/21 16hr: 15min:20Sec, End date/time: 12/12/21 20hr: 10min:10Sec, Product ID: XYZABC124, Start date /time : 13/12/21 10hr: 12min:25Sec, Scan date/time: 13/12/21 16hr: 15min:20Sec, End date/time: 13/12/21 20hr: 10min:10Sec.
Accordingly, the artisan profile 240 includes artisan data i.e. the first signature; the processed gestures i.e. the secondary signatures; and, location, timestamp of the artisan 105 i.e. the third signature.
Referring to FIG. 3, the schematic of for identification of hand-made artifacts using gesture recognition 100 is described. The system 100 includes a unique label 305, a label module 310, the wearable device 125 and the pro-cessing unit 135. The wearable device 125 includes a plurality of sensors 315, for sensing the data and a processor 325 for processing the data. The label module 310 includes label data 320 of an artisan 105 that includes artisan pro-file 240 and a fourth signature i.e. the artifact signature. The processing unit 135 includes a database 360, a raw data 330 i.e. secondary signature and third signature, a machine learning module 335 and an analytics module 340. The database 360 stores validated and authenticated details of artisans 105.
The wearable devices 125 encapsulates plurality of sensors 315 for ex-ample an audio sensor, an accelerometer, a gyroscopic sensor and the like. The wearable devices 125 are wirelessly connected with the handheld device 130. The plurality of sensors 315 records the gestures 205 when worn by an artisan 105 while crafting an artifact 140. The processor 325 processes the data record-ed by the wearable device 125. The artisan 105 logs into the handheld device 130 and verifies the authenticity by entering the respective login details. The wearable device 125 than transmits the processed data to the handheld device 130 wirelessly.
The unique label 305 is embedded within the artifact 140 at predefined position during the process of crafting or after crafting the artifact 140. The unique label 305 is preferably a barcode, a RFID tag or a NFC chip in this pre-ferred embodiment. However, the type of label 305 varies in other embodi-ments of the present invention. In accordance with the present invention, the unique label 305 is configured by deriving details from the first user profile that is defined by the first, the second, the third signatures, and the fourth signa-ture. Accordingly, the unique label 305 includes the artisan profile 240 and the fourth signature. It is understood that the label data 320 includes artisan de-tails, artisan gestures, the location details, time stamp of the crafted artifact, and the like. The label data 320 is accessed through scanning the label 305 through camera of the handheld device 130, a NFC reader, RFID Reader or the like.
The processing unit 135 receives raw data 330 from the wearable devic-es 125 through the handheld device 130. The raw data 330 is unprocessed data along with redundant data. The raw data 330 is stored in the database 345 for analysis. The raw data 330 is then processed with statistical methods and /or in machine learning module 335. In the machine learning module 335 the Artifi-cial Intelligence, machine learning and deep learning algorithms are applied for extracting useful data from the raw data 330. The extracted data from the ma-chine learning module 335 is forwarded towards the analytics module 340.
The analytics module 340 creates a unique profile of the artisan 105 based on the data and stores in the artisan profile 240 that includes artisan data i.e. the first signature; the processed gestures i.e. the secondary signatures; and, loca-tion, timestamp of the artisan 105 i.e. the third signature. The origin validation module 350 validates the origin of the artisan 105 by comparing the data of the artisan profile with the standard database of approved artisans 105 . The analyt-ical module 340 also asses various parameters such as, total productivity of the artisan 105, number of stitches inserted by artisan 105 in a fixed amount of time, and quantum of work done on the product such as stitches inserted in each piece which may help in pricing and costing or deriving carbon credits, among other uses.
Referring to FIG. 4 and FIG. 4A an operational flow of system and method 100 is described hereinafter. In an initial step 405, the artisan 105 wears a wearable device 140 while crafting an artifact. In a next step 410, the artisan 105 authenticates on the handheld device 130 by entering appropriate login details. In a next step 415, the wearable device 125 is connected with the handheld device 130 through wireless connectivity. In a next step 420, the wearable device 125 records the gestures through plurality of sensors 315.
In a next step 425, the plurality of sensors 315 collect data and send the raw data 330 to the processing unit 135. In a next step 430, the processing unit 135 receives the raw data 330 from the artisan 105 and stores in the database 345. In a next step 435, the raw data 330 is processed with statistical methods and /or on the machine learning module 335. In this step 435, the useful data is extracted from the raw data 330. In a next step 440, the useful data is analyzed and the artisan profile 240 is created.
In a next step 445, the predefined data from artisan profile is extracted and stored in origin validation module. In a next step 450, the artisan data is embedded onto the unique label 305 and the unique label 305 which is already embedded into the artifact 140. In a next step 455, the consumer 120, the re-tailer 115 and the intermediary 110 scan the label 305 through their handheld device 130 camera to receive the details and trace the originality of the hand-made products.
Now referring to FIG. 5 and 5a, the graphical representation of artisans 105 gestures in hand spinning activity and hand embroidery activity is shown. It is noted that, this graphical representation defines the secondary signature in accordance with the present invention. Accordingly, it is understood that the authentication of the artifact done by the user by scanning the label with the help of handheld device is advantageously based on the unique gestures or body movements associated with each of the crafts the data is utilized to au-thenticate the product to be handmade.
The analytical module 340 is configured with the secondary signature to assess various parameters such as, total productivity of the artisan 105, number of stitches inserted by artisan 105 in a fixed amount of time, and quantum of work done on the product such as stitches inserted in each piece which may help in pricing and costing or deriving carbon credits, among other uses. For example, the productivity of an artisan 105 working on artifact 140 during a shift of 8 hours was found to be 65%. In another example actual time an artisan worked in a day was found to be 5 hours and 20 minutes. In another case the number of stiches inserted by artisan in a shift of 8 hours was found to be 2120 numbers. Similarly, in another example an artifact contained a total of 1250 stitches.
The embodiments were chosen and described in order to best explain the principles of the present invention and its practical application, to thereby enable others, skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use con-templated.
It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the scope of the present invention.
, Claims:CLAIMS:
We claim:
1. A system 100 for identification of hand-made artifacts using gesture recognition by artisans 105 characterized in that comprising:
an artifact 140 crafted by the artisans 105 including a unique la-bel 305 for identifying authenticity of the artifact 140;
a wearable device 125 worn by the artisans 105 while crafting an artifact 140, the wearable device 125 including plurality of sensors 315 for capturing gestures from the artisan 105; and
a processing unit 135 for processing the data received through plurality of sensors 315, the processing unit includes a database 345 for storing raw data 330, an analytics module 340 for processing the raw data 330, a machine learning module 335 for processing the raw data 330, an artisan profile 240 for storing artisan data; and the label 305 configured by deriving data from an artisan profile 240, a handheld device 130 scanning the label 305 for authenticating the arti-fact in the system 100.
2. The system 100 as claimed in claim 1 wherein, the artisan profile 240 in-cluding a primary signature configured to including artisan data.
3. The system 100 as claimed in claim 1 wherein, the artisan profile 240 including a secondary signature configured to encode gestures 205 of the artisan 105.
4. The system 100 as claimed in claim 1 wherein, the artisan profile 240 in-cluding a third signature configured to including artisan details.
5. The system 100 as claimed in claim 1 wherein, the artisan profile 240 in-cluding a fourth signature configured to include artifact data.
6. The system 100 as claimed in claim 1 wherein, the machine learning module 335 being configured with artificial intelligence, deep learning algorithms for extracting useful data.
7. The system 100 as claimed in claim 1 wherein, the analytics module 340 analyses useful data and creates the artisan 105 profile.
8. The system 100 as claimed in claim 1 wherein, includes an origin valida-tion module 350 configured to trace the origin of the artisan 105.
9. The system 100 as claimed in claim 1 wherein, the analytics module 340 assessing total productivity of the artisan 105 on the artifact 140.
10. A method 100 for identification of hand-made artifacts using gesture recognition of the system 100 as claimed in claim 1 comprising the steps of:
a. Wearing the wearable device 125;
b. Receiving, artisan data from the primary signature;
c. Recording, gestures 205 of artisan 105 through plurality of sen-sors 315;
d. Encoding, set of gestures 205 of the artisan 105 from the sec-ondary signature;
e. Receiving, artisan 105 data from the third signature;
f. Creating, artisan profile 240 by processing first, second and third signatures on the processor 325;
g. Processing the raw data 330 using machine learning module 335;
h. Extracting useful data from raw data 330 and creating artisan profile 240;
i. Embedding label data 320 into the label 305; and
j. Embedding label 305 in the artifact 140.
11. The method 100 as claimed in claim 10, wherein the consumer 120 accessing the label data 320 by scanning the label 305 of the artifact through the handheld device 130.
Dated this 20th April 2022 For KOSHA DESIGNS
Anand Mahurkar
(IN/PA-1862)
Agent for the Applicant