Recogni Inc.

États‑Unis d’Amérique

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Type PI
        Brevet 13
        Marque 7
Juridiction
        International 12
        Europe 7
        États-Unis 1
Date
2026 mars 1
2026 (AACJ) 1
2025 3
2024 2
2022 1
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Classe IPC
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion 4
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques 3
G06F 17/16 - Calcul de matrice ou de vecteur 2
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques 2
G06N 3/08 - Méthodes d'apprentissage 2
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1.

AI ACCELERATOR INTEGRATED CIRCUIT CHIP WITH INTEGRATED CELL-BASED FABRIC ADAPTER

      
Numéro d'application US2025043474
Numéro de publication 2026/059726
Statut Délivré - en vigueur
Date de dépôt 2025-08-26
Date de publication 2026-03-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Goldman, Gary, S.
  • Anand, Ramalingam, K.
  • Venkataraman, Kalyana, S.
  • Ozceri, Berend
  • Jorinipally, Pradeep, R.
  • Lau, Chung, Y.
  • Savla, Jigar, K.
  • Radhakrishnan, Ashwin
  • Davie, Michael
  • Li, Shijun

Abrégé

An integrated circuit formed on (i) a single semiconductor die or (ii) a plurality semiconductor dies that are integrated into a single package. The integrated circuit may include a communication interface including a serializer/deserializer (SerDes) interface; a fabric adapter communicatively coupled to the communication interface; a plurality of inference engine clusters, each inference engine cluster including a respective memory element and/or memory interface; and a data interconnect communicatively coupling each respective memory element and/or memory interfaces of the plurality of inference engine clusters to the fabric adapter. The fabric adapter may be configured to facilitate remote direct memory access (RDMA) read and write services and/or datagram communication over a cell¬ based switch fabric to and from the respective memory elements and/or memory interfaces of the plurality of inference engine clusters via the data interconnect.

Classes IPC  ?

  • G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p. ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié

2.

SYSTEMS AND METHODS FOR PERFORMING MATRIX MULTIPLICATION WITH A PLURALITY OF PROCESSING ELEMENTS

      
Numéro d'application US2024024806
Numéro de publication 2025/116950
Statut Délivré - en vigueur
Date de dépôt 2024-04-16
Date de publication 2025-06-05
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Huang, Jian, Hui
  • Goldman, Gary, S.

Abrégé

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

Classes IPC  ?

3.

MULTI-MODE ARCHITECTURE FOR UNIFYING MATRIX MULTIPLICATION, 1X1 CONVOLUTION AND 3X3 CONVOLUTION

      
Numéro d'application US2024024809
Numéro de publication 2025/116952
Statut Délivré - en vigueur
Date de dépôt 2024-04-16
Date de publication 2025-06-05
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Huang, Jian Hui
  • Goldman, Gary S.

Abrégé

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

Classes IPC  ?

  • G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p. ex. plusieurs processeurs de données à instruction unique
  • G06F 9/54 - Communication interprogramme

4.

MULTI-MODE ARCHITECTURE FOR UNIFYING MATRIX MULTIPLICATION, 1X1 CONVOLUTION AND 3X3 CONVOLUTION

      
Numéro d'application US2024024808
Numéro de publication 2025/116951
Statut Délivré - en vigueur
Date de dépôt 2024-04-16
Date de publication 2025-06-05
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Huang, Jian Hui
  • Goldman, Gary S.

Abrégé

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

Classes IPC  ?

5.

Multiply accumulate (MAC) unit with split accumulator

      
Numéro d'application 18408296
Numéro de brevet 12039290
Statut Délivré - en vigueur
Date de dépôt 2024-01-09
Date de la première publication 2024-07-16
Date d'octroi 2024-07-16
Propriétaire Recogni Inc. (USA)
Inventeur(s)
  • Huang, Jian Hui
  • Goldman, Gary S.

Abrégé

In a multiply accumulate (MAC) unit, an accumulator may be implemented in two or more stages. For example, a first accumulator may accumulate products from the multiplier of the MAC unit, and a second accumulator may periodically accumulate the running total of the first accumulator. Each time the first accumulator's running total is accumulated by the second accumulator, the first accumulator may be initialized to begin a new accumulation period. In one embodiment, the number of values accumulated by the first accumulator within an accumulation period may be a user-adjustable parameter. In one embodiment, the bit width of the input of the second accumulator may be greater than the bit width of the output of the first accumulator. In another embodiment, an adder may be shared between the first and second accumulators, and a multiplexor may switch the accumulation operations between the first and second accumulators.

Classes IPC  ?

  • G06F 7/544 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs n'établissant pas de contact, p. ex. tube, dispositif à l'état solideMéthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul
  • G06F 7/509 - AdditionSoustraction en mode parallèle binaire, c.-à-d. ayant un circuit de maniement de chiffre différent pour chaque position pour opérandes multiples, p. ex. intégrateurs numériques

6.

METHODS AND SYSTEMS FOR PROCESSING READ-MODIFY-WRITE REQUESTS

      
Numéro d'application US2023015130
Numéro de publication 2024/035446
Statut Délivré - en vigueur
Date de dépôt 2023-03-13
Date de publication 2024-02-15
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Goldman, Gary, S.
  • Radhakrishnan, Ashwin

Abrégé

A memory system comprises a plurality of memory sub-systems, each with a memory bank and other circuit components. For each of the memory sub-systems, a first buffer receives and stores a read-modify -write request (with a read address, a write address and a first operand), a second operand is read from the memory bank at the location specified by the read address, a combiner circuit combines the first operand with the second operand, an activation circuit transforms the output of the combiner circuit, and the output of the activation circuit is stored in the memory bank at the location specified by the write address. The first operand and the write address may be stored in a second buffer while the second operand is read from the memory bank. Further, the output of the activation circuit may be first stored in the first buffer before being stored in the memory bank.

Classes IPC  ?

  • G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
  • G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement

7.

LOW POWER HARDWARE ARCHITECTURE FOR A CONVOLUTIONAL NEURAL NETWORK

      
Numéro d'application US2021048078
Numéro de publication 2022/051191
Statut Délivré - en vigueur
Date de dépôt 2021-08-27
Date de publication 2022-03-10
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Huang, Jian Hui
  • Bodwin, James Michael
  • Joginipally, Pradeep, R.
  • Abhiram, Shabarivas
  • Goldman, Gary, S.
  • Patz, Martin, Stefan
  • Feinberg, Eugene, M.
  • Ozceri, Berend

Abrégé

Dynamic data quantization may be applied to minimize the power consumption of a system that implements a convolutional neural network (CNN). Under such a quantization scheme, a quantized representation of a 3x3 array of m-bit activation values may include 9 n- bit mantissa values and one exponent shared between the n-bit mantissa values (n < m); and a quantized representation of a 3x3 kernel with p-bit parameter values may include 9 q-bit mantissa values and one exponent shared between the q-bit mantissa values (q < p). Convolution of the kernel with the activation data may include computing a dot product of the 9 n-bit mantissa values with the 9 q-bit mantissa values, and summing the shared exponents. In a CNN with multiple kernels, multiple computing units (each corresponding to one of the kernels) may receive the quantized representation of the 3x3 array of m-bit activation values from the same quantization-alignment module.

Classes IPC  ?

  • G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
  • G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

8.

RECOGNI VISION COGNITION MODULE

      
Numéro d'application 018485072
Statut Enregistrée
Date de dépôt 2021-06-04
Date d'enregistrement 2021-09-23
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

9.

RECOGNI VCM

      
Numéro d'application 018455952
Statut Enregistrée
Date de dépôt 2021-04-19
Date d'enregistrement 2021-08-26
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

10.

VCM

      
Numéro d'application 018455954
Statut Enregistrée
Date de dépôt 2021-04-19
Date d'enregistrement 2021-11-10
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment; all of the aforesaid goods only for use in the field of object recognition for self-driving cars and autonomous and assisted driving of vehicles.

11.

Miscellaneous Design

      
Numéro d'application 018455956
Statut Enregistrée
Date de dépôt 2021-04-19
Date d'enregistrement 2021-08-26
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

12.

RECOGNI REALTIME OBJECT RECOGNITION

      
Numéro d'application 018455957
Statut Enregistrée
Date de dépôt 2021-04-19
Date d'enregistrement 2021-08-26
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

13.

RECOGNI

      
Numéro d'application 018455958
Statut Enregistrée
Date de dépôt 2021-04-19
Date d'enregistrement 2021-08-26
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

14.

CLUSTER COMPRESSION FOR COMPRESSING WEIGHTS IN NEURAL NETWORKS

      
Numéro d'application US2019017781
Numéro de publication 2019/177731
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Backhus, Gilles, J.C.A.
  • Feinberg, Eugene, M.

Abrégé

FKCFFFF biases of the first layer in the memory, and classifying data received by the convolutional neural network.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

15.

DETERMINISTIC LABELED DATA GENERATION AND ARTIFICIAL INTELLIGENCE TRAINING PIPELINE

      
Numéro d'application US2019017784
Numéro de publication 2019/177733
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Abhiram, Shabarivas
  • Feinberg, Eugene, M.

Abrégé

Systems, methods, and machine-readable media for deterministically generating labeled data for training or validating machine learning models for image analysis are described. Approaches described herein allow this training data to be generated, for example, in real time, and in response to the conditions at the location where images are generated by image sensors.

Classes IPC  ?

  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
  • G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
  • G06N 3/08 - Méthodes d'apprentissage

16.

SYSTEMS AND METHODS FOR INTER-CAMERA RECOGNITION OF INDIVIDUALS AND THEIR PROPERTIES

      
Numéro d'application US2019017786
Numéro de publication 2019/177734
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s) Abhiram, Shabarivas

Abrégé

Systems, methods, and machine-readable media for using a convolutional neural network to generate hash strings corresponding to object instances, and thereby use the characteristic hash strings to recognize the same object instance depicted in images generated at different times and by different camera devices.

Classes IPC  ?

  • G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales

17.

REAL-TO-SYNTHETIC IMAGE DOMAIN TRANSFER

      
Numéro d'application US2019017782
Numéro de publication 2019/177732
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Backhus, Gilles, J.C.A.
  • Abhiram, Shabarivas
  • Feinberg, Eugene, M.

Abrégé

Systems, methods, and machine-readable media for deterministically generating labeled data for training or validating machine learning models for image analysis, and for using such machine learning models to determine the contents of real-domain images by using a domain transfer to synthetic-appearing images are described.

Classes IPC  ?

  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

18.

EFFICIENT CONVOLUTIONAL ENGINE

      
Numéro d'application US2019017787
Numéro de publication 2019/177735
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s) Feinberg, Eugene, M.

Abrégé

A hardware architecture for implementing a convolutional neural network.

Classes IPC  ?

  • G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
  • G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

19.

THREE-DIMENSIONAL ENVIRONMENT MODELING BASED ON A MULTICAMERA CONVOLVER SYSTEM

      
Numéro d'application US2019017789
Numéro de publication 2019/177736
Statut Délivré - en vigueur
Date de dépôt 2019-02-13
Date de publication 2019-09-19
Propriétaire RECOGNI INC. (USA)
Inventeur(s)
  • Abhiram, Shabarivas
  • Backhus, Gilles, J.C.A.
  • Feinberg, Eugene, M.
  • Ozceri, Berend
  • Patz, Martin, Stefan

Abrégé

Systems, methods, and machine-readable media for determining a three-dimensional environment model of the environment of one or more camera devices, in which image processing for inferring the model may be performed at the camera devices, are described.

Classes IPC  ?

  • G06T 7/579 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir du mouvement
  • G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments

20.

RECOGNI

      
Numéro d'application 018098218
Statut Enregistrée
Date de dépôt 2019-07-22
Date d'enregistrement 2019-12-04
Propriétaire Recogni Inc. (USA)
Classes de Nice  ? 09 - Appareils et instruments scientifiques et électriques

Produits et services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.