Patent Drafting Analysis of Facebook’s Language Cluster ML Training System | US 10,685,188 B1
Patent Drafting Analysis of Facebook's Language Cluster ML Training System | US 10,685,188 B1
A structural and strategic analysis of Facebook's granted patent on training machine learning models for language clusters, examining claim architecture, drafting quality, prosecution positioning, and critical gaps.
Spec Words
8,200
Across 6 sections
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Total Claims
20
3 independent · 17 dependent
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Figure Sheets
8
System modules, flow diagrams, social network architecture
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Published by PatSnap Insights Team · · 12 min read Verified by PatSnap Eureka Data
Overview
Structural Overview
The detailed description dominates at approximately 63% of total specification words (~5,200 of ~8,200), providing thorough coverage of the language cluster determination, machine learning training, and internationalization modules. The claim set comprises 20 claims total — 3 independent claims covering a computer-implemented method (Claim 1), a system (Claim 11), and a non-transitory computer readable medium (Claim 16) — with 17 dependent claims providing layered refinements. Eight drawing sheets cover system module architecture, process flow diagrams, and a social networking deployment environment, with FIG. 7's generic computer hardware diagram adding limited claim-specific support.
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 8 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1 | System architecture showing the machine learning internationalization module 102 containing language cluster determination module 104, language cluster machine learning module 106, and content item classification module 108, with data store 120.Search in Eureka ↗ | System architecture |
| FIG. 2A | Language cluster determination module 202 with four sub-modules: language similarity determination 204, social behavior similarity determination 206, cluster generation 208, and representative language selection 210.Search in Eureka ↗ | System architecture |
| FIG. 2B | Language cluster machine learning module 252 showing machine learning training module 254, machine learning evaluation module 256, and internationalization module 258.Search in Eureka ↗ | System architecture |
| FIG. 3 | End-to-end scenario diagram showing data flow from languages 302 through language similarity determination 304 and social behavior similarity determination 308, to language cluster generation 312, representative language selection 316, model training 322, and classification 324.Search in Eureka ↗ | Claim support |
| FIG. 4 | Three-step flow diagram for the first method: generate language clusters (402), determine representative language (404), and train machine learning model to classify content items (406).Search in Eureka ↗ | Flow diagram |
| FIG. 5 | Two-step flow diagram for the second method: obtain a content item in a non-representative cluster language (502) and determine classification using the cluster's machine learning model (504).Search in Eureka ↗ | Flow diagram |
| FIG. 6 | Social networking system 630 deployment diagram showing web server 632, API request server 634, user profile store 636, connection store 638, action logger 640, authorization server 644, machine learning internationalization module 646, user device 610, and external system 620 connected via network 650.Search in Eureka ↗ | System architecture |
| FIG. 7 | Generic computer system 700 hardware diagram showing processor 702, cache 704, host bridge 710, high performance I/O bus 706, standard I/O bus 708, I/O bus bridge 712, system memory 714, network interface 716, mass storage 718, and I/O ports 720.Search in Eureka ↗ | Other |
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Claims
Claim Architecture Analysis
The patent contains 3 independent claims: Claim 1 (computer-implemented method), Claim 11 (system), and Claim 16 (non-transitory computer readable medium/CRM), providing tripartite coverage across all standard software patent claim types. The dependent-to-independent ratio of 5.67:1 is typical for software/AI patents in this IPC class, though the dependent claims are heavily concentrated on refinements to language cluster generation rather than on the classification step. Notably, the CRM claim (Claim 16) mirrors the method claim closely, and the system claim (Claim 11) carries its own dependent chain through Claims 12–15.
Core inventive concept: The claims address the computational inefficiency of training separate machine learning models for every language by grouping languages into clusters based on "language similarity" or "social behavior similarity," selecting a "representative language" for each cluster, training a single model on that representative language, and then classifying content items in other cluster languages by first machine-translating their text "to the representative language" before applying the model — as recited in Claims 1, 11, and 16.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A computer-implemented method | comprising | generating plurality of language clusters based on language similarity or social behavior similarity; determining a representative language that is one of the human languages in the cluster; training a machine learning model based on the representative language; classifying a content item in a non-representative cluster language based on applying the ML model to a machine translation of the content item's text into the representative languageSearch prior art ↗ |
| Claim 11 | A system | comprising | at least one hardware processor; memory storing instructions that cause the system to perform: generating language clusters, determining representative language, training ML model, classifying non-representative language content items via machine translation to representative languageSearch prior art ↗ |
| Claim 16 | A non-transitory computer readable medium including instructions that, when executed by at least one hardware processor of a computing system, cause the computing system to perform a method | comprising | generating language clusters based on language similarity or social behavior similarity; determining representative language as one of the human languages in the cluster; training ML model based on representative language; classifying non-representative cluster language content item via ML model applied to machine translation of text to representative languageSearch prior art ↗ |
Claim Dependency Tree
1 Computer-implemented method: generate language clusters via similarity, select representative language, train ML model, classify via machine translation to representative languageSearch Claim 1 prior art ↗
2 Adds: language similarity determined by quality of machine translation between first and second human languageSearch in Eureka ↗
3 Further: quality of machine translation indicated by score based on comparison to one or more human reference translationsSearch in Eureka ↗
4 Adds: social behavior similarity determined based on features of user interactions with content items in each languageSearch in Eureka ↗
5 Further: determining social behavior similarity includes embedding feature vectors in a feature spaceSearch in Eureka ↗
6 Further: features include number of comments, reactions, comment through rate, reaction through rate, or comment entropySearch in Eureka ↗
7 Adds: cluster generation based on combination of language similarity and social behavior similaritySearch in Eureka ↗
8 Adds: cluster generation includes embedding feature vectors for languages in a corresponding feature space including language and social behavior similarity featuresSearch in Eureka ↗
9 Adds: training data includes plurality of content items in representative language and classifications associated with those content itemsSearch in Eureka ↗
10 Adds: further determining the content item is engagement bait based at least in part on classifying the content itemSearch in Eureka ↗
11 System: hardware processor and memory storing instructions to generate clusters, determine representative language, train ML model, classify via machine translationSearch Claim 11 prior art ↗
12 Adds: language similarity determined by quality of machine translation between first and second human languageSearch in Eureka ↗
13 Further: quality indicated by score based on comparison to one or more human reference translationsSearch in Eureka ↗
14 Adds: social behavior similarity determined based on features of user interactions with content items in each languageSearch in Eureka ↗
15 Further: features include number of comments, reactions, comment through rate, reaction through rate, or comment entropySearch in Eureka ↗
16 Non-transitory CRM: instructions to generate clusters, determine representative language, train ML model, classify via machine translation to representative languageSearch Claim 16 prior art ↗
17 Adds: language similarity determined by quality of machine translation between first and second human languageSearch in Eureka ↗
18 Further: quality indicated by score based on comparison to one or more human reference translationsSearch in Eureka ↗
19 Adds: social behavior similarity determined based on features of user interactions with content items in each languageSearch in Eureka ↗
20 Further: features include number of comments, reactions, comment through rate, reaction through rate, or comment entropySearch in Eureka ↗
| Metric | This Application | Software / Cloud Norm |
|---|---|---|
| Total claims | 20 | 15 – 25 |
| Independent claim count | 3 | 2 – 4 |
| Dependent : Independent ratio | 5.67 : 1 | 4 – 8 : 1 |
| Method claims present? | Yes — Claim 1 | Always |
| System / apparatus claims? | Yes — Claim 11 | Common |
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Drafting Quality
Drafting Quality Signals
The claim set demonstrates strong tripartite coverage across method, system, and CRM formats, and the machine-translation-to-representative-language classification mechanism in Claims 1, 11, and 16 provides a concrete technical step that supports §101 eligibility. However, the dependent claims for Claims 11 and 16 are largely verbatim copies of the Claims 2–6 structure (without the engagement bait fallback of Claim 10), and the specification's internationalisation module description — covering machine translation of training data and auto-generation of cluster-language training data — is not captured in any claim.
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Antecedent Basis
The claim language maintains clean antecedent basis throughout the 20-claim set. In Claim 1, "the plurality of language clusters" is properly introduced by "generating...a plurality of language clusters," and "the representative language" traces back to "determining...a representative language." The term "the language cluster" in the classifying step consistently references the antecedent "a language cluster of the plurality of language clusters" introduced in the determining step — no orphaned definite articles were identified across Claims 1–20.
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Spec–Claim Consistency
FIG. 3 and the corresponding detailed description directly support the core independent claim limitations: language cluster generation maps to blocks 304/308/312, representative language selection maps to block 316, model training maps to block 320, and classification maps to block 324. The social behavior feature vector embedding limitation in Claim 5/8 is supported by the cluster generation module 208 discussion and FIG. 2A. The machine translation classifying step in Claims 1, 11, and 16 is supported by the internationalization module 258 discussion, though FIG. 5 only abstractly references block 504 without naming the translation step explicitly.
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Transition Word Usage
All three independent claims use "comprising" as the transition, which is strategically appropriate for software/AI patents — it preserves open-ended scope, allowing infringement even if a system adds additional steps or components beyond those claimed. The dependent claims also use "wherein" correctly to add limitations without changing the transition. No "consisting of" or "consisting essentially of" language appears, which is correct for this technology domain where competitor implementations may include additional processing steps.
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§112(f) Means-Plus-Function Risk
Claim 11 recites "a memory storing instructions that, when executed by the at least one processor, cause the system to perform" — this is a standard software-on-hardware claiming format that avoids "means for" language and thus does not trigger §112(f) directly. However, the spec-level description of the language cluster determination module 104, language cluster machine learning module 106, and content item classification module 108 as named functional modules (FIG. 1) without corresponding claim language could invite §112(f) treatment if claims were ever amended to recite these module names directly. The current claim language avoids this risk but the named-module architecture in the spec creates a latent vulnerability if continuation claims pursue module-level claiming.
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§101 Eligibility Risk
The independent claims carry moderate Alice/Mayo exposure because the core steps — generating clusters, selecting a representative language, training a model, and classifying — could be characterised as abstract mathematical/mental processes. The primary §101 defense lies in the concrete machine translation limitation in Claims 1, 11, and 16: "classifying...based at least in part on an application of the machine learning model to a machine translation of text of the content item, wherein the text is machine translated to the representative language" — this provides a specific, non-abstract technological implementation step. Claim 10's engagement-bait detection adds a concrete real-world application, but this limitation does not appear in the system (Claim 11) or CRM (Claim 16) independent claims, weakening §101 defense parity across claim types.
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Dependent Claim Fallback Quality
The dependent claims provide meaningful fallback on the cluster-generation methodology — Claims 2–3 add machine-translation quality scoring, Claims 4–6 add social behavior feature vectors, Claim 7 adds the combination of both similarity types, and Claim 8 adds feature space embedding — all of which are valuable prosecution fallbacks. However, Claim 10 (engagement bait detection) is the sole dependent claim adding a specific downstream application use case, and it depends only from Claim 1, leaving Claims 11 and 16 without equivalent application-specific fallback. Additionally, Claims 12–15 and 17–20 are mechanical mirrors of Claims 2–6, offering no new fallback positions beyond format variation.
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Abstract Quality
The abstract reads: "Systems, methods, and non-transitory computer readable media can generate a plurality of language clusters based on one or more of: language similarity between languages or social behavior similarity between languages. A representative language for a language cluster...can be determined. For the language cluster...a machine learning model can be trained based on the representative language...to classify content items in languages included in the language cluster." While accurate, the abstract omits the novel machine-translation-based classification mechanism — the specific step of translating non-representative language content to the representative language before applying the model — which is the key technical contribution that distinguishes this from prior art single-model approaches. An examiner reading only the abstract may underestimate the claim scope.
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Figure Support Quality
The core claim limitations are well-supported across FIGs. 1–5: FIG. 1 supports the system architecture of Claim 11, FIG. 2A supports the cluster determination sub-steps in Claims 2–8, FIG. 2B supports the training and internationalization steps, FIG. 3 provides the most comprehensive end-to-end process support for Claims 1, 11, and 16, and FIGs. 4–5 provide method flow support. The machine translation classification step — "the text is machine translated to the representative language" — is depicted in FIG. 3's classification block 324 but is not shown as a distinct flow step in FIG. 4, creating a minor gap in figure-to-claim traceability for this critical limitation.
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Scorecard
Strategic Intent Scorecard
Multi-dimensional assessment of this application's patent strategy quality, based on claim structure, specification depth, and prosecution positioning.
Claim Breadth
3.5
Prosecution Defensibility
3.8
Spec–Claim Consistency
4
Dependent Claim Coverage
3
Claim Type Diversity
4.5
Figure Support Quality
3.5
Key observation: Claim Type Diversity scores highest (4.5/5) because the filing covers all three canonical software patent formats — method (Claim 1), system (Claim 11), and CRM (Claim 16) — providing enforcement flexibility across the full range of infringing implementations. Dependent Claim Coverage scores lowest (3.0/5) because Claims 12–15 and 17–20 are structural mirrors of Claims 2–6 that add no new technical fallback beyond format variation, and Claim 10's engagement-bait application is not replicated in the system or CRM chains. Practitioners should note that a continuation filing adding application-specific dependent claims (e.g., click-bait detection, hate speech classification) to Claims 11 and 16, and claiming the auto-generated training data technique from the internationalization module 258, would meaningfully strengthen the portfolio.
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Critical Gaps
3 Critical Gaps in This Claim Set
A senior-attorney lens on the three highest-priority structural weaknesses — what each exposes in prosecution and litigation, and what a stronger filing would have done differently.
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3 Critical Gaps in This Claim Set
See the full attorney-level analysis of what this application leaves unprotected — and how to draft it more defensively for your own filings.
Machine translation limitation over-narrows claims Auto-generated training data unclaimed Centroid-based representative language selection unclaimed
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Disclaimer: This analysis is generated by PatSnap Eureka AI based on publicly available patent data from the USPTO. It does not constitute legal advice and should not be relied upon as such. Patent data may be subject to change as prosecution progresses. Scores and assessments reflect automated analysis and may not capture all relevant legal or technical nuances. Always consult a qualified patent attorney for formal legal opinions on patentability, freedom to operate, or infringement.
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