生成AIの統計データ

2024年9月30日 22:01

GenAIについて勉強してるけど、イマイチ網羅性が無いとG検定を受験しました。終わったのでサボリ気味の記事も生成AIを中心にまたアップします。
まずは生成AI、国ごとの比率を下記に載せ、やっぱ中国と示します。

この度は、2024年9月7日（土）開催の JDLA Deep Learning for GENERAL 2024#5を受験頂き、ありがとうございました。

下記の通り受験結果をお知らせ致します。
■合否結果
=================
【　合　格　】
=================

総受験者数　4,917名
合格者数　　3,689名

メール

ということで、最低限の理解は有ると証明？したところで、面白い生成AIの統計データを見つけたのでご紹介します。

Patent Landscape Report: Generative Artificial Intelligence. (wipo.int)

国際機関からのレポートです。

WIPOの考える２０２３年以降のAIニュース

2023 • January - MusicML generates songs from prompts (Agostinelli et al. 2023)
2023 • February - Google unveils its experimental conversational AI service, Bard
2023 • March - GPT-4.0 can handle images and significantly more text than its predecessor (OpenAI 2023)
2023 • July - Meta release Llama 2, an open-source large language model, available at no cost for research
and commercial use
2023 • December - Axel Springer comes to an agreement with OpenAI, The New York Times sues OpenAI
and Microsoft for copyright infringement
2024 • February - OpenAI presents Sora, an LLM that can generate videos up to a minute long with high
visual quality from user prompts (Brooks et al. 2024)
2024 • April - Meta introduces Llama 3, pretrained on over 15 trillion tokens, 50 times more than GPT-3 and
7 times more than Llama 2
2024 • May - Open AI releases GPT-4o, which can accept and generate multimodal (text, audio, image,
video) outputs

リリースされた日時がキレイにまとまっています。訴訟まであるのが知財由来らしいですね。

機械学習について、そこに喰わせるデータセットのランキングも載ってます。

コーパスでアクセスされている（HOT）なソフト

コーパスのランキングでも、ChatGPTはやはり強しです。

A28. Top 50 PCT clusters, 2019–2023
Note: For further details on methodology, refer to the Special theme of the 2020 edition of the PCT Yearly Review. Data presented in previous years may vary slightly due to constant improvements in geocoding.
Source: WIPO Statistics Database, March 2024.

企業別特許出願

生成AI、濃い青が過去十年、薄い青が直近２年の出願件数ランキング上位企業の件数です。
ここでも中国🇨🇳企業、テンセント、バァドゥ、アリババの御三家が上位です。

Most companies own patents in one dominant GenAI model, however, there are some 47 exceptions such as Tencent and Google who have filed patents in several model types. Table 4 Top patent owners in GenAI models (companies), 2014–2023

なお、生成AIの分野では論文が活況ですが、ここではアルファベットがダントツです。ホームページでは論文が無料公開されているので、興味のある方は見てみてください。

Similar to companies, universities/research organizations file patents in predominantly one type of model, with GAN being the most preferred type. Table 5 Top patent owners in GenAI models (universities/research organizations), 2014–2023

では生成AIといっても画像なのか大規模言語モデルなのかを調べたのが下の図です。

内訳の詳細
企業を横串に比較してます。

その２

出願件数ランキング上位の注力領域

Note: The table shows published GenAI patent families between 2014 and 2023. A large proportion of GenAI patents does not fit into any of the specific models, as these patents do not contain keywords relating to the specific model used in the patent abstract, claims or title. Therefore, the total number of GenAI patent families is larger than the sum of the five models. Source: WIPO, based on patent data from EconSight/IFI Claims, April 2024

じゃあ分野ごとの内訳、何処の企業が出願しているのかというと

まとめ

いつも通り中国🇨🇳スゲーになりました。また注力領域は微妙に異なり
テンセントが画像
バァドゥがＧＡＮ
アリババがＬＬＭ
と自分のプラットフォームに合わせて居るようです。
日系企業はかろうじて、NTTとSONYがランクインしてました。
上の人からお代が来たら、上のリンクを開いて見てはいかがでしようか?

オマケ

GenAI AI totalの分類✕キーワード
ここから下は調査担当以外には念仏以下だと思うので読む必要無し
担当は仕事頼まれた時はここから抜粋しとけば70点は貰えるはず

(TITLEABSTRACTCLAIMS=(GENERATIVE* NEAR3 (AI OR ARTIFICIAL INTEL* OR ADVERSARIAL) OR GENERATIVE SEQ2 PRE_TRAINED NEAR3 (LANGUAGE SEQ2 MODEL OR TRANSFORMER?) OR CHAT_GPT OR VARIATIONAL SEQ2 AUTOENCODER? OR GENERATIVE SEQ2 ADVERSARIAL SEQ2 NETWORK* OR DIFFUSION SEQ2 PROBABILISTIC SEQ2 MODEL? OR AUTO_REGRESSIV* SEQ2 MODEL* ) OR (( IPC=(G06F 18/214, G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04, G06V 10/70, G10L 13, G16H 30/40, H04L 51/02) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") OR CPC=(G06F 18/214, G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04, G06T2207/20, G06V 10/70, G10L 13, G16H 30/40, H04L 51/02) ) AND ( TITL EABSTRACTCLAIMS=(GENERATIVE* NEAR3 (AI OR ARTIFICIAL INTEL* OR ADVERSARIAL) OR GENERATIVE SEQ2 PRE_TRAINED NEAR3 (LANGUAGE SEQ2 MODEL OR TRANSFORMER?) OR CHATGPT OR VARIATIONAL SEQ2 AUTOENCODER? OR CONVOLUTIONAL SEQ2 GENERATIVE SEQ2 ADVERSARIAL SEQ2 NETWORK* OR DIFFUSION SEQ2 PROBALISTIC* SEQ2 MODEL? OR DIFFUSIONAL SEQ2 NETWORK? OR DENOISING SEQ2 DIFFUSION SEQ2 PROBABILISTIC SEQ2 MODEL? OR GENERATIVE SEQ2 LATENT SEQ2 OPTIMIZATION OR NEURAL SEQ2 RADIANCE SEQ2 FIELD? OR AUTO_REGRESSIV* NEAR3 MODEL* OR GAN OR GANS OR GENAI OR VAE OR VAES OR (DENOISING OR VIDEO OR MODELS OR STABLE) NEAR3 DIFFUSION* SEQ2 MODEL* OR VARIATIONAL NEAR3 AUTOENCODER* OR GPT_3* OR GPT_4* ) OR ( TITLEABSTRACTCLAIMS=( ((IMAGE* OR TEXT* OR VIDEO* OR SPEECH* OR ("3D" MODEL*) OR GENE SEQ* OR DESIGN OR (PROGRAM* OR COMPUTER* OR SOFTWARE*) SEQ2 CODE* OR MUSIC* OR SPEECH* OR SCENE* OR MOLECULE* OR SYNTHETIC SEQ2 DATA* OR WORD SEQ3 SEQUENC*) NEAR5 (GENERATE* OR GENERATING* OR GENERATION* OR GENERATIV*)) OR GENERATIVE* NEAR3 MODEL*) AND ( TITLEABSTRACTCLAIMS=((TRANSFORMER* OR AUTOENCODER* OR LLM OR LARGE SEQ2 LANGUAGE SEQ2 MODEL* OR GAN OR GENERAT* SEQ2 ADVERSARIAL SEQ2 NETWORK* OR (AUTO_REGRESSIV* OR DIFFUSION SEQ2 PROBALISTIC) SEQ2 MODEL*)) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\C5.3.23. GAN, GENERATIVE ADVERSARIAL NETWORKS", "ECONSIGHT TECHNOLOGY FIELDS\IC5.3.27. AUTOREGRESSIV MODELS", "ECONSIGHT TECHNOLOGY FIELDS\IC5.3.28. VARIATIONAL AUTOENCODER,VAE", "ECONSIGHT TECHNOLOGY FIELDS\ IC5.3.29. DIFFUSION MODELS", "ECONSIGHT TECHNOLOGY FIELDS\IC5.3.36. LARGE LANGUAGE MODELS,LLM") ))))) OR ( IPC=(G06N 3/0475) OR CPC=(G06N 3/0475))

Generative adversarial networks
(TITLEABSTRACTCLAIMS=(GENERATIVE NEAR5 ADVERSARIAL OR GAN OR (GENERATIVE SEQ2 ADVERSARIAL SEQ2 NETWORK*) OR (DUELING OR CONTRARIAN* OR ANTAGONISTIC OR ADVERSARIAL) NEAR3 (NEURAL SEQ2 NETWORK*)) ) AND ( FTERMSMART=(5L096/HA11) OR IPC=(G06N 3/02, G06V 10/70) OR CPC=(G06N 3/02, G06T2207, G06V 10/70))

Variational autoencoder
(CPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR IPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") ) AND TITLEABSTRACTCLAIMS=(VARIATIONAL NEAR5 AUTO_ENCODER* OR VAE )

Autoregressive models
(CPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR IPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") ) AND TITLEABSTRACTCLAIMS=(((AUTO_REGRESSIV* OR AUTO_REGRESSION OR SELF_REGRESSIV* OR SELF_REGRESSION* OR RECURSIVE_REGRESSION* OR ITERATIVE FORECASTING) NEAR3 MODEL*))

Patent Landscape Report – Generative Artificial Intelligence 78 Large language models

ＬＬＭを調べる時はこれ
以下、ＧＡＮや、生成AIの検索式と続きます。

(CPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04, G10L 15/183) OR IPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04, G10L 15/183) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\EC\5IC\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") ) AND TITLEABSTRACTCLAIMSDESCRIPTION=(LARGE SEQ2 LANGUAGE SEQ2 MODEL* OR LLM OR LARGE LANGUAGE MODEL* OR (LARGE LANGUAGE MODEL* OR LLM OR LARGE NEAR3 (LANGUAGE SEQ2 MODEL*) OR ((EXTENSIVE* OR MASSIVE OR LARGE OR GIGANTIC OR IMMENSE OR COLLOSSAL) SEQ2 (LANGUAGE OR LINGUISTIC OR SPEECH OR VERBAL) SEQ2 (MODEL* )) OR SUBSTANTIAL LANGUAGE PROCESSOR* ))

Diffusion models
(CPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR IPC=(G06F 40/20, G06F 40/284, G06F 40/40, G06N 3/02, G06N 3/08, G06N 20/00, G06Q 10/04) OR TAG=("ECONSIGHT TECHNOLOGY FIELDS\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") ) AND TITLEABSTRACTCLAIMS=(DIFFUSION NEAR5 MODEL* OR PROBALISTIC NEAR3 MODEL* OR (STABLE* OR DENOISING) NEAR3 DIFFUSION* OR (DIFFUSION NEAR3 MODEL* OR (PROGAGATION OR DIFFUSION ) SEQ2 (STOCHASTIC* OR PROBALISTIC* OR PROBABILITY) SEQ2 MODEL* OR PROPAGATION SEQ2 STOCHASTIC SEQ2 MODEL* OR DISPERSION SEQ2 STOCHASTIC SEQ2 MODEL* OR SCORE_BASED SEQ2 GENERATIVE SEQ2 MODEL*))

GenAI modes: Image, video
( CPC=(A61B 5/1128, B23Q 17/249, G01M 11/065, G01N 15/1463, G01N 15/1475, G01N 21/8851, G01N2203/0647, G02B 7/365, G02B 21/244, G05D 1/0251, G06F 16/583, G06F 16/5862, G06F 16/70, G06F 16/78, G06F 16/783, G06F 16/7864, G06F2212/455, G06T, G06T 1, G06T 1/20, G06T 3, G06T 3/4046, G06T 5, G06T 7, G06T 9, G06T 9/002, G06T 11, G06T 13, G06T 15, G06T 17, G06T 19, G06T2207, G06T2207/00, G06T2207/20, G06T2207/20081, G06T2207/20084, G06V 10/70, H04N 5/2226, H04N 5/23229, H04N 5/23254, H04N2013/0074, Y10S 128/922, Y10S 707/914) OR FTERMSMART=(2H029/CD01, 2H029/DB12, 2H070/BB12, 2H095/AC02, 2H106/AA83, 2H109/BA06, 2K103/BB05, 2K203/GB22, 4C161/WW04, 4C601/JC, 5B057/DA, 5B057/DB, 5B057/DC, 5C020/ AA13, 5C079/LA40, 5C122/FH, 5C122/FH17, 5C122/FH18) OR IPC=(G06F 16/58, G06F 16/583, G06F 16/70, G06F 16/78, G06F 16/783, G06T, G06T 1, G06T 1/20, G06T 3, G06T 5, G06T 7, G06T 9, G06T 11, G06T 13, G06T 15, G06T 17, G06T 19, G06V 10/70) ) OR( TITLEABSTRACTCLAIMS=((IMAGE OR VIDEO) NEAR3 (SYNTHES* OR CREAT* OR GENERAT*) OR IMAGE_TO_IMAGE OR IMAGE STYLE TRANSFER* OR TEXT_TO_IMAGE* OR VIDEO_TO_VIDEO*) ) AND ( IPC=(G06F, G06T, G06T 1, G06T 3, G06T 7, G06T 9, G06T 13, G06T 15, G06T 17, G06T 19, G06V) OR CPC=(A61B 5/1128, B23Q 17/249, G01M 11/065, G01N 15/1463, G01N 15/1475, G01N 21/8851, G01N2203/0647, G02B 7/365, G02B 21/244, G05D 1/0251, G06F, G06F 16/70, G06F2212/455, G06T, G06T 1, G06T 3, G06T 7, G06T 9, G06T 13, G06T 15, G06T 17, G06T 19, G06T2207, G06T2207/00, G06V, H04N 5/2226, H04N 5/23229, H04N 5/23254, H04N2013/0074, Y02D 10/00, Y10S 128/922, Y10S 707/914) ) Text ( CPC=(G05B2219/13106, G06F 16/243, G06F 16/24522, G06F 16/3329, G06F 16/3334, G06F 16/3335, G06F 16/3337, G06F 16/3338, G06F 16/3344, G06F 16/3347, G06F 16/345, G06F 16/36, G06F 16/367, G06F 16/374, G06F 16/90332, G06F 17/20, G06F 40, G06F 40/16, G06F 40/20, G06F 40/205, G06F 40/279, G06F 40/30, G06F 40/40, G06F 40/56) OR IPC=(G06F 16/36, G06F 17/20, G06F 40, G06F 40/16, G06F 40/20, G06F 40/205, G06F 40/279, G06F 40/30, G06F 40/40, G06F 40/56) ) OR ( TITLEABSTRACTCLAIMS=(PARAPHRASING OR (REWORDING OR REPHRASING) NEAR5 (WORD* OR SENTENCE* OR PARAGRAPH*) OR (SEMANTIC* OR NATURAL NEAR3 LANGUAGE) OR LANGUAGE NEAR3 (GENERAT* OR PRODUCTI*) OR TEXT* NEAR3 SUMMARI?ATI*) ) AND ( IPC=(G06F) OR CPC=(G06F, Y02D 10/00) ) Speech, music, voice ( CPC=(A63B2071/068, A63F2300/1081, B60G2401/19, B60R 25/257, B65H2551/132, B66B2201/4646, G01C 21/3608, G03G2215/00122, G05B2219/40531, G06F 3/167, G06F 16/7834,

Appendices G10H 1/0025, G10H2210, G10H2240, G10H2250, G10L, G10L 13, G10L 15, G10L 15/08, G10L 15/24, 79 G10L 15/26, G10L 17, G10L 25, G10L 99, H04M 1/642, H04M 1/6505, H04M2201/39, H04M2201/40, H04Q2213/13378, H04Q2213/378, Y10S 379/907) OR IPC=(G10L, G10L 13, G10L 15, G10L 15/08, G10L 15/24, G10L 15/26, G10L 17, G10L 25, G10L 99) OR FTERMSMART=(2C028/BB07, 2H270/QA36, 5B056/HH05, 5B089/KH16, 5C164/PA43, 5C164/PA46, 5D015, 5D045, 5D102/HC33, 5D108/BC17, 5H220/GG06, 5K015/AA06, 5K025/EE26, 5K027/HH20, 5K034/FF07, 5K038/GG04, 5K049/CC10, 5K127/CA27, 5K201/EC09) ) OR( TITLEABSTRACTCLAIMS=((VOICE OR SPEECH) NEAR3 (SYNTHESI* OR GENERAT* OR ANALYSIS*) OR SPEECH_TO_TEXT OR TEXT_TO_SPEECH* OR SPEECH* NEAR4 RECOG* OR VOICE* NEAR4 RECOG* OR VOICE* NEAR4 PRINT* OR VOICEPRINT*) ) AND ( IPC=(G06F) OR CPC=(G06F, G06F 3/01, Y02D 10/00) ) Software, code ( CPC=(G06F 8/00, G06F 8/20, G06F 8/30, G06F 8/40, G06F 8/60, G06F 8/70, G06F 11/36) OR IPC=(G06F 8/00, G06F 8/20, G06F 8/30, G06F 8/40, G06F 8/60, G06F 8/70, G06F 11/36) ) OR ( TITLEABSTRACTCLAIMS=((SOFTWARE OR CODE) NEAR3 (COMPLETION* OR GENERAT* OR DEVELOPMENT* OR PROGRAMMING)) ) AND ( IPC=(G06F) OR CPC=(G06F, Y02D 10/00) )

3D image models

(( CPC=(G06T 13/20, G06T 13/40, G06T 15, G06T 15/04, G06T 15/20, G06T 17, G06T 17/20, G06T 19, G06T 19/003, G06T 19/20) OR IPC=(G06F 3/04815, G06T 13/20, G06T 13/40, G06T 15, G06T 15/04, G06T 15/20, G06T 17, G06T 17/20, G06T 19, G06T 19/20) OR FTERMSMART=(5B050/BA09, 5B050/ EA27) ) OR ( TITLEABSTRACTCLAIMS=(TEXT_TO_NERF OR TEXT_TO_3D OR (((THREE_D OR "3D") NEAR3 IMAGE*) NEAR3 MODEL*)) ))

Molecules, genes, proteins
( IPC=(C40B, C40B 10, C40B 20, C40B 30, C40B 30/00, C40B 30/02, C40B 30/04, C40B 30/08, C40B 30/10, C40B 40, C40B 50, C40B 50/02, C40B 60, C40B 70, C40B 80, C40B 99, G06F 19/10, G06F 19/16, G06F 19/18, G06F 19/22, G06F 19/28, G16B, G16B 20/00, G16B 30/00, G16B 40/00, G16C, G16C 10, G16C 20, G16C 60, G16C 99, G16CMISS) OR TITLEABSTRACTCLAIMSDESCRIPTION=((C OMPUTATIONAL W2 CHEMISTRY) OR CHEMINFORMATICS OR ((AB D2 INITIO) AND CHEM*) OR (DENSITY D2 FUNCTIONAL D2 THEORY) OR (MOLECULAR D2 MECHANICS) OR (QUANTUM D2 CHEMISTRY) OR CHEMOINFORMATICS OR (DRUG* NEAR6 (DEVELOPMENT* OR TARGETING* OR DESIGN*)) NEAR10 (COMPUT* OR SOFTWARE* OR ALGORITHM*) OR (SYSTEMS* NEAR6 BIOLOGY*) OR PHARMACOPHOR*) OR CPC=(B01J2219/00689, B01J2219/00695, C04B2235/6026, C07K2299, C12N 15/1037, C12N 15/1093, C40B, C40B 10, C40B 20, C40B 30/00, C40B 30/02, C40B 30/04, C40B 30/08, C40B 30/10, C40B 40, C40B 50, C40B 50/02, C40B 60, C40B 70, C40B 80, C40B 99, G06F 19/10, G06F 19/16, G06F 19/18, G06F 19/22, G06F 19/28, G06F 19/70, G06F 19/706, G16B, G16B 20/00, G16B 30/00, G16B 40/00, G16C, G16CMISS, Y10S 423/05, Y10S 977/808) ) OR ( ( IPC=(C01, C07B, C07C, C07D, C07F, C07G, C08, C21) OR CPC=(C01, C07B, C07C, C07D, C07F, C07G, C08, C21) ) AND TAG=("ECONSIGHT TECHNOLOGY FIELDS\IC5.3.9. NEURAL NETWORKS & DEEP LEARNING") )

GenAI applications: Physical sciences and engineering TITLEABSTRACTCLAIMS= ((PHYSICAL NEAR2 SCIENCES) OR (ARCHAEOLOGY) OR (ASTRONOMY) OR (CHEMISTRY) OR ((EARTH OR ATMOSPHERIC) NEAR2 SCIENCES) OR (ENVIRONMENTAL NEAR2 SCIENCES) OR (COMPUTER_AIDED DESIGN) OR (PHYSICS) OR (MATHEMATICS) OR (ELECTRONICS) OR (WIRELESS DEVICE?)) OR IPC=(C OR D OR E OR F01 OR F02 OR F03 OR F04 OR F15 OR F16 OR F17) OR CPC=(G16C20/70 OR C OR D OR E OR F01 OR F02 OR F03 OR F04 OR F05 OR F15 OR F16 OR F17) Industry and manufacturing TITLEABSTRACTCLAIMS= (INDUSTRY OR INDUSTRIAL OR (SUPPLY NEAR3 CHAIN) OR MANUFACTURING OR (MACHINE NEAR3 TOOL?)) OR CPC=(G06Q10/06 OR G06Q10/08 OR G06Q50/04 OR G06Q50/28) OR IPC=(G06Q10/06 OR G06Q10/08 OR G06Q50/04 OR G06Q50/28) Patent Landscape Report – Generative Artificial Intelligence 80 Life and medical sciences TITLEABSTRACTCLAIMS= ((LIFE NEAR2 SCIENCE?) OR HEALTH OR BIOLOGY OR HEALTHCARE OR MEDICAL OR (COMPUTATIONAL BIOLOGY) OR (MOLECULAR NEAR2 ("SEQUENCE ANALYSIS" OR EVOLUTION)) OR (RECOGNITION NEAR2 GENES) OR TRANSCRIPTOMICS OR "BIOLOGICAL NETWORKS" OR GENOTYPING OR PROTEOMICS OR GENOMICS OR BIOINFORMATICS OR METABOLOMICS OR METABONOMICS OR GENETICS OR PROTEOMICS OR TRANSCRIPTOMICS OR (DRUG DISCOVERY)) OR CPC=(G16B40 OR A61 OR G16H50/20) OR IPC=(A61 OR G16B40 OR G16H50/20) Telecommunications TITLEABSTRACTCLAIMS=(TELECOM? OR TELEPHON? OR PHONE? OR (COMMUNICATION? NEAR2 NETWORK?) OR RADIO OR PHONE? OR WIRELESS OR (COMMUNICATION NEAR2 SATELLITE?) OR TELEVISION) OR CPC=(H04L2012/5686 OR H04L2025/03464 OR H04L25/0254 OR H04L25/03165 OR H04L41/16 OR H04L45/08 OR H04N21/4662 OR H04Q2213/054 OR H04Q2213/13343 OR H04Q2213/343 OR H04R25/507) OR IPC=(H04L12/70 OR H04L25/02 OR H04L25/03 OR H04L12/24 OR H04L12/751 OR H04N21/466 OR H04R25)

Transportation TITLEABSTRACTCLAIMS= (TRANSPORTATION OR VEHICLE? OR AEROSPACE OR SPACECRAFT OR SPACEFLIGHT OR ROADS OR AUTOMOBILE? OR AUTOMOTIVE? OR TRUCKS OR RAILWAYS OR TRAINS OR FREIGHT OR AIRWAYS OR WATERWAYS OR WATERCRAFT? OR AVIONICS OR AERONAUTICS OR AIRCRAFT? OR DRONE? OR UAV OR HELICOPTER? OR BOAT OR BOATS OR (BUS NEAR2 STATION?) OR AUTOBUS OR MOTORBUS OR STREETCAR OR TROLLEY) OR CPC=(B60W30/06 OR B60W30/10 OR B60W30/12 OR B60W30/14 OR B60G2600/1876 OR B60G2600/1878 OR B60G2600/1879 OR B62D015/0285 OR B64G2001/247 OR G06T2207/30248 OR G06T2207/30236 OR G05D001 OR B64C2201) OR IPC=(B60W30/06 OR B60W30/10 OR B60W30/12 OR B60W30/14 OR B62D15/02 OR B64G1/24 OR G05D1) Energy management TITLEABSTRACTCLAIMS=(((ENERGY OR POWER) NEAR2 (MANAGEMENT OR PLANNING OR CHALLENGE)) OR -GRID? OR ( NEAR2 GRID?)) OR CPC=(G01R 31/2846, G01R 31/2848, G01R 31/3651, G21, H01J2237/30427, H01M 8/04992, H02, H02H 1/0092, H02P 21/0014, H02P 23/0018, H03H2017/0208, H03H2222/04, H04W 52) OR IPC=(G21, H01M 8/04992, H02, H03H 17/02, H04W 52)

Agriculture TITLEABSTRACTCLAIMS=((AGRICULTURE OR AGRICULTURAL OR CULTIVATE* OR BREEDING OR AGRONOMY OR PESTICIDE? OR AGROCHEMICHAL? OR FERTILIZER?)) OR CPC=(A01) OR IPC=(A01) Security TITLEABSTRACTCLAIMS= (SECURITY OR SURVEILLANCE OR (INVESTIGATION TECHNIQUES) OR (EVIDENCE COLLECTION) OR (NETWORK FORENSICS) OR (SYSTEM FORENSICS) OR (DATA RECOVERY) OR (COMPUTER FORENSICS) OR (BIOMETRICS) OR (CYBERSECURITY)) OR CPC=(G06F21 OR A61B5/117 OR H04W 12) OR IPC=(G06F21 OR A61B5/117 OR H04W 12)

Entertainment TITLEABSTRACTCLAIMS= (ENTERTAINMENT OR ((VIDEO OR COMPUTER OR ELETRONIC OR ONLINE) NEAR2 (GAME? OR GAMING))) OR CPC=(A63) OR IPC=(A63) Business solutions TITLEABSTRACTCLAIMS=((ELECTRONIC NEAR2 (COMMERCE? OR "DATA INTERCHANGE" OR "FUNDS TRANSFER")) OR (ENTERPRISE NEAR2 (COMPUTING OR "INFORMATION SYSTEMS" OR "RESOURCE PLANNING" OR APPLICATIONS OR (ARCHITECTURE NEAR2 (MANAGEMENT Appendices OR FRAMEWORKS OR MODELING)) OR ONTOLOGIES OR TAXONOMIES OR VOCABULARIE OR 81 "DATA MANAGEMENT" OR INTEROPERABILITY)) OR (CUSTOMER NEAR2 SERVICE?) OR (DIGITAL CASH) OR (E-COMMERCE INFRASTRUCTURE) OR (ONLINE NEAR2 (SHOPPING OR BANKING OR AUCTIONS)) OR (SECURE ONLINE TRANSACTIONS) OR (MARKETING) OR (VIDEO CONTENT DISCOVERY) OR (RECRUITMENT) OR (INTRANETS) OR (EXTRANETS) OR (DATA CENTERS) OR ((BUSINESS PROCESS) NEAR2 (MANAGEMENT OR MODELING OR MONITORING OR " CROSS-ORGANIZATIONAL")) OR (BUSINESS NEAR2 (INTELLIGENCE OR RULES)) OR ((SERVICE[1]ORIENTED OR IT OR EVENT-DRIVEN) SEQ2 ARCHITECTURES) OR (BUSINESS-IT ALIGNMENT) OR (IT GOVERNANCE) OR (INFORMATION NEAR2 (INTEGRATION OR INTEROPERABILITY))) OR CPC=(G06Q 10/10, G06Q 20, G06Q 30) OR IPC=(G06Q 10/10, G06Q 20, G06Q 30) Military TITLEABSTRACTCLAIMS=(MILITARY OR WARFARE OR CYBERWARFARE OR TACTICAL OR TACTICS OR ARMY OR WEAPON? OR BATTLE? OR BATTLEFIELD? OR PEACE OR PEACEKEEPING) OR CPC=(B63G, B64D 7, F41, F42, G01S 19/18) OR IPC=(B63G, B64D 7, F41, F42, G01S 19/18)

Education
TITLEABSTRACTCLAIMS= (EDUCATION OR EDUCATIONAL OR (DIGITAL NEAR2 LIBRARY) OR ((CHILD? OR CHILDREN OR PERSON OR PEOPLE OR STUDENT?) NEAR2 INSTRUCTION?) OR ((INTERACTIVE OR COLLABORATIVE OR DISTANCE) NEAR2 LEARNING) OR E-LEARNING OR (LEARNING MANAGEMENT SYSTEM?)) OR CPC=(G09B OR G06Q50/20) OR IPC=(G09B OR G06Q50/20)

Document management and publishing (TITLEABSTRACTCLAIMS=((DOCUMENT NEAR2 (MANAGEMENT OR EDITING OR PROCESSING OR SEARCHING OR METADATA OR CAPTURE OR ANALYSIS OR SCANNING OR SCRIPTING OR PREPARATION)) OR (TEXT? NEAR2 (MANAGEMENT OR EDITING OR PROCESSING OR SEARCHING)) OR (VERSION CONTROL) OR (GRAPHICS NEAR2 (RECOGNITION OR INTERPRETATION)) OR (((OPTICAL CHARACTER) OR (ONLINE HANDWRITING)) NEAR2 RECOGNITION) OR (MARKUP LANGUAGE?) OR (HYPERTEXT LANGUAGE?) OR (ANNOTATION) OR ((MULTIMEDIA OR MIXED-MEDIA) NEAR2 CREATION) OR (IMAGE COMPOSITION) OR ((HYPERTEXT OR HYPERMEDIA) NEAR2 CREATION)) OR TITLEABSTRACTCLAIMS=((PUBLISH ING OR (COPY NEAR2 EDITING) OR PUBLICATION? OR EDITORIAL) OR ((AFFECTIVE NEAR2 COMPUTING) OR (AFFECTIVE NEAR2 (RECOGNITION OR ESTIMATION OR STATE?)) OR ((ARTIFICIAL NEAR2 EMOTION*) NEAR2 INTELLIGENCE) OR ((PHYSIOLOGICAL NEAR3 MARKER) NEAR3 RECOGNITION) OR (EMOTION NEAR2 AI))) OR CPC=(A61B 5/165, G06F 40/10, G10L 25/63) AND IPC=(G06F 40/10, G10L 25/63))

Personal devices, computing and HCI ( TITLEABSTRACTCLAIMS=((PERSONAL NEAR2 COMPUTER?) OR (WORD NEAR2 PROCESSOR?) OR SPREADSHEETS OR MICROCOMPUTER? OR (HUMAN-MACHINE) OR (TOUCH NEAR2 SCREEN?) OR ((DISPLAY OR DISPLAYS) NEAR2 (TECHNOLOGY OR SYSTEM? OR APPARATUS)) OR (USER NEAR2 INTERFACE?)) OR TITLEABSTRACTCLAIMS=((AFFECTIVE NEAR2 COMPUTING) OR (AFFECTIVE NEAR2 (RECOGNITION OR ESTIMATION OR STATE?)) OR ((ARTIFICIAL NEAR3 EMOTION??) NEAR3 INTELLIGENCE) OR ((PHYSIOLOGICAL NEAR3 MARKER) NEAR3 RECOGNITION) OR (EMOTION NEAR2 AI)) ) OR IPC=(G10L 25/63) OR CPC=(A61B 5/165, G10L 25/63)

Banking and finance TITLEABSTRACTCLAIMS=(FINTECH OR BANKING OR FINANCE OR FINANCING OR INSURANCE? OR REINSURANCE? OR INSURABLE? OR TRADING OR LIABILITY) OR CPC=(G06Q40) OR IPC=(G06Q40) Arts and humanities TITLEABSTRACTCLAIMS= ((FINE ARTS) OR (PERFORMING ARTS) OR (ARCHITECTURE NEAR2 BUILDING?) OR (LANGUAGE TRANSLATION) OR (MEDIA ARTS) OR (MUSIC?) OR CINEMA OR Patent Landscape Report – Generative Artificial Intelligence 82 CINEMATOGRAPHY OR MOVIE OR WRITTING OR PAINTING? OR SCULPTING OR PHOTOGRAPHY OR THEATRE) Computing in government TITLEABSTRACTCLAIMS=(GOVERNMENT OR VOTING OR ELECTION OR E-GOVERNMENT OR (PUBLIC NEAR2 (POLICY OR POLICIES))) OR CPC=(G06Q50/26) OR IPC=(G06Q50/26) Networks/smart cities TITLEABSTRACTCLAIMS=(((SOCIAL OR DEVICE?) NEAR2 NETWORK?) OR IOT OR (INTERNET NEAR2 THINGS) OR SMART_CITY OR SMART_CITIES OR (SMART NEAR2 (CITY OR CITIES OR GRID? OR HOME? OR TRANSPORT? OR DEVICE? OR SENSOR?)) OR (VIRTUAL NEAR2 PLANTS))

Cartography
TITLEABSTRACTCLAIMS=(CARTOGRAPHY OR GEOGRAPHIC? OR TOPOGRAPHY OR TOPOGRAPHICS) OR IPC=(G06F 16/29) OR CPC=(G06F 16/29) Industrial property, law, social and behavioral sciences TITLEABSTRACTCLAIMS= (((BEHAVIOR OR BEHAVIORAL) NEAR2 SCIENCE?) OR (SOCIAL NEAR2 SCIENCE?) OR (LEGAL NEAR3 (STUDIES OR KNOWLEDGE OR INFORMATION? OR DOCUMENT? OR EVALUATION? OR CITATION? OR OPINION? OR TEXT? OR ARGUMENT? OR CONSULTANCY OR RIGHT? OR ISSUE? OR RISK? OR RESEARCH?? OR MATTER? OR CASE? OR JUDGMENT? OR DISCUSSION? OR CONCEPT? OR ACTION? OR STANDARD?)) OR LAWYER? OR JUDICIAL? OR LEGISLATION? OR ANTHROPOLOGY OR ETHNOGRAPHY OR PSYCHOLOGY OR ECONOMICS OR SOCIOLOGY)

A.5 Prompts Almost 100 prompts for various concepts of GenAI and GenAI application areas were used in EconSight’s advanced AI search algorithms to help retrieve GenAI patents with high recall.
The prompts below were used as the second stage in the GenAI patent retrieval approach discussed in detail in Appendix

A.1. Concept_1 = "Generative AI, generative artificial intelligence for 3D creation or three[1]dimensional designs." Concept_2 = "Generative AI, generative artificial intelligence for accounting." Concept_3 = "Generative AI, generative artificial intelligence in Biotech." Concept_4 = "Generative AI, generative artificial intelligence for Advertisement." Concept_5 = "Generative AI, generative artificial intelligence for AI understanding." Concept_6 = "Generative AI, generative artificial intelligence for algorithm discovery." Concept_7 = "Generative AI, generative artificial intelligence for analysts."

参考特許

Example patent: WO2023172817 – SYSTEMS AND METHODS FOR A CONVERSATIONAL FRAMEWORK OF PROGRAM SYNTHESIS Applicant: Salesforce Abstract: Embodiments described herein provide a program synthesis framework that generates code programs through a multi-turn conversation between a user and a system. Specifically, the description to solve a target problem is factorized into multiple steps, each of which includes a description in natural language (prompt) to be input into the generation model as a user utterance. The model in turn synthesizes functionally correct subprograms following the current user utterance and considering descriptions and synthesized subprograms at previous steps. The subprograms generated at the multiple steps are then combined to form an output of program in response to the target problem.

Example patent: US20230229866 – SYSTEM AND METHOD FOR MANAGEMENT OF LIFE CYCLE OF CONTRACTS Applicant: Tata Consultancy Service

Example patent: US20210294341 – METHOD AND APPARATUS FOR GENERATING U-TURN PATH IN DEEP LEARNING-BASED AUTONOMOUS VEHICLE Applicants: Hyundai, Kia Abstract: A method for generating a U-turn path in an autonomous vehicle includes calculating a drivable area, generating multiple paths drivable in the drivable area, filtering a driving strategy path among the multiple paths based on deep learning, and determining a final path from the filtered candidate paths.

IZING AN INTELLIGENT PAINTING PIPELINE FOR IMPROVED BRUSHSTROKE SEQUENCES Applicant: Adobe Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating painted digital images utilizing an intelligent painting process that includes progressive layering, sequential brushstroke guidance, and/or brushstroke regularization. For example, the disclosed systems utilize an image painting model to perform progressive layering to generate and apply digital brushstrokes in a progressive fashion for different layers associated with a background canvas and foreground objects. In addition, the disclosed systems utilize sequential brushstroke guidance to generate painted foreground objects by sequentially shifting through attention windows for regions of interest in a target digital image. Furthermore, the disclosed systems utilize brushstroke regularization to generate and apply an efficient brushstroke sequence to generate a painted digital image. Image: Source: PATENTSCOPE. Computing in government Gen AI’s ability to access, organize and leverage data will create new possibilities for improving government offerings. For example, customer services could get a boost from GenAI-powered chatbots that answer questions from or customize services for residents. Alternatively, when working on citizens’ service requests, GenAI can also assist government employees by unlocking data across agencies to provide information and services more intuitively. Another area that can benefit is government procurement that traditionally is a complex and time-consuming process, involving multiple steps, stakeholders and complex legal and regulatory constraints. GenAI could help to simplify and automate procurement processes by providing intelligent recommendations and facilitating negotiations. Example patent: CN115526440 – RISK MANAGEMENT ASSESSMENT METHOD BASED ON CROWD SIMULATION Applicants: Hitachi, Tsinghua University

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